hypothesis generation diagram

Hypothesis Maker Online

Looking for a hypothesis maker? This online tool for students will help you formulate a beautiful hypothesis quickly, efficiently, and for free.

Are you looking for an effective hypothesis maker online? Worry no more; try our online tool for students and formulate your hypothesis within no time.

• 🔎 How to Use the Tool?
• ⚗️ What Is a Hypothesis in Science?

👍 What Does a Good Hypothesis Mean?

• 🧭 Steps to Making a Good Hypothesis

🔗 References

📄 hypothesis maker: how to use it.

Our hypothesis maker is a simple and efficient tool you can access online for free.

If you want to create a research hypothesis quickly, you should fill out the research details in the given fields on the hypothesis generator.

Below are the fields you should complete to generate your hypothesis:

• Who or what is your research based on? For instance, the subject can be research group 1.
• What does the subject (research group 1) do?
• What does the subject affect? - This shows the predicted outcome, which is the object.
• Who or what will be compared with research group 1? (research group 2).

Once you fill the in the fields, you can click the ‘Make a hypothesis’ tab and get your results.

⚗️ What Is a Hypothesis in the Scientific Method?

A hypothesis is a statement describing an expectation or prediction of your research through observation.

It is similar to academic speculation and reasoning that discloses the outcome of your scientific test . An effective hypothesis, therefore, should be crafted carefully and with precision.

A good hypothesis should have dependent and independent variables . These variables are the elements you will test in your research method – it can be a concept, an event, or an object as long as it is observable.

You can observe the dependent variables while the independent variables keep changing during the experiment.

In a nutshell, a hypothesis directs and organizes the research methods you will use, forming a large section of research paper writing.

Hypothesis vs. Theory

A hypothesis is a realistic expectation that researchers make before any investigation. It is formulated and tested to prove whether the statement is true. A theory, on the other hand, is a factual principle supported by evidence. Thus, a theory is more fact-backed compared to a hypothesis.

Another difference is that a hypothesis is presented as a single statement , while a theory can be an assortment of things . Hypotheses are based on future possibilities toward a specific projection, but the results are uncertain. Theories are verified with undisputable results because of proper substantiation.

When it comes to data, a hypothesis relies on limited information , while a theory is established on an extensive data set tested on various conditions.

You should observe the stated assumption to prove its accuracy.

Since hypotheses have observable variables, their outcome is usually based on a specific occurrence. Conversely, theories are grounded on a general principle involving multiple experiments and research tests.

This general principle can apply to many specific cases.

The primary purpose of formulating a hypothesis is to present a tentative prediction for researchers to explore further through tests and observations. Theories, in their turn, aim to explain plausible occurrences in the form of a scientific study.

It would help to rely on several criteria to establish a good hypothesis. Below are the parameters you should use to analyze the quality of your hypothesis.

🧭 6 Steps to Making a Good Hypothesis

Writing a hypothesis becomes way simpler if you follow a tried-and-tested algorithm. Let’s explore how you can formulate a good hypothesis in a few steps:

Step #1: Ask Questions

The first step in hypothesis creation is asking real questions about the surrounding reality.

Why do things happen as they do? What are the causes of some occurrences?

Your curiosity will trigger great questions that you can use to formulate a stellar hypothesis. So, ensure you pick a research topic of interest to scrutinize the world’s phenomena, processes, and events.

Step #2: Do Initial Research

Carry out preliminary research and gather essential background information about your topic of choice.

The extent of the information you collect will depend on what you want to prove.

Your initial research can be complete with a few academic books or a simple Internet search for quick answers with relevant statistics.

Still, keep in mind that in this phase, it is too early to prove or disapprove of your hypothesis.

Step #3: Identify Your Variables

Now that you have a basic understanding of the topic, choose the dependent and independent variables.

Take note that independent variables are the ones you can’t control, so understand the limitations of your test before settling on a final hypothesis.

Step #4: Formulate Your Hypothesis

You can write your hypothesis as an ‘if – then’ expression . Presenting any hypothesis in this format is reliable since it describes the cause-and-effect you want to test.

For instance: If I study every day, then I will get good grades.

Step #5: Gather Relevant Data

Once you have identified your variables and formulated the hypothesis, you can start the experiment. Remember, the conclusion you make will be a proof or rebuttal of your initial assumption.

So, gather relevant information, whether for a simple or statistical hypothesis, because you need to back your statement.

Step #6: Record Your Findings

Finally, write down your conclusions in a research paper .

Outline in detail whether the test has proved or disproved your hypothesis.

Edit and proofread your work, using a plagiarism checker to ensure the authenticity of your text.

We hope that the above tips will be useful for you. Note that if you need to conduct business analysis, you can use the free templates we’ve prepared: SWOT , PESTLE , VRIO , SOAR , and Porter’s 5 Forces .

❓ Hypothesis Formulator FAQ

Updated: Jul 19th, 2024

• How to Write a Hypothesis in 6 Steps - Grammarly
• Forming a Good Hypothesis for Scientific Research
• The Hypothesis in Science Writing
• Scientific Method: Step 3: HYPOTHESIS - Subject Guides
• Hypothesis Template & Examples - Video & Lesson Transcript

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Hypothesis Generator

Write about, want some more features.

• - History to store generated content
• - Access to mobile apps for content generation on the go
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Exploring the AI4Chat Hypothesis Generator

An overview.

AI4Chat's Hypothesis Generator is a cutting-edge tool that fosters innovation and creativity in problem-solving and research. With just a click, this feature presents unique and perceptive hypothesis, enabling users to delve deeper into their field of interest.

Functionality and Application

The Hypothesis Generator is embedded within the AI4Chat platform, functioning as a cognitive assistive tool. By interrelating complex variables within specified parameters, the generator can derive novel and significant hypotheses. It plays an indispensable role in various fields, benefiting researchers, students, scientists, and problem-solvers, to generate insights and streamline decision-making processes.

Integrated Features

AI4Chat's Hypothesis Generator also boasts integrated features such as chat synchronization across all devices, labels, categories, notes, chat description, search, and dark mode. These features enhance the user experience by offering convenient and efficient tools that simplify the hypothesis generation process.

Accessibility and User Interface

AI4Chat strives for accessibility and ease of use, offering the Hypothesis Generator on its mobile applications for Android and iOS as well as its website. With a user-friendly interface, navigating through the features and tools becomes effortless. The dark mode feature allows for a comfortable viewing experience, making hypothesis generation an intuitive process.

Questions about AI4Chat? We are here to help!

For any inquiries, drop us an email at [email protected] . We’re always eager to assist and provide more information.

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• 🔗 Sharable Chat Link: Generate a link to share your chat, allowing others to view the conversation.
• 🌍 Multilingual Chat in 75+ Languages: Communicate and generate content in over 75 languages, expanding your global reach.
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• 📁 AI Chat with Files and Images: Upload images or files and ask questions related to their content. AI automatically understands and answers questions based on the content or context of the uploaded files.
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AI Hypothesis Generator

Hypothesis Generator to help you come up with a boilerplate hypothesis for your test ideas. Generate well-structured hypothesis in under 10 seconds!

1. Give us a brief about your hypothesis...

Hypotheses in A/B Testing

Hypotheses form an integral part of A/B Testing. They provide a clear path and expected outcome for the test, based on the initial conditions, such as the user interface and user experience, among others. A well-defined hypothesis is the foundation of any successful A/B test, guiding the direction of the test and serving as a benchmark against which the test’s results are evaluated.

What are the benefits?

The Automated Hypothesis Creator simplifies the first step in the A/B testing process and provides several benefits:

• Quick and efficient hypothesis generation.
• Saves time and resources which can often be invested in analysing the output of the A/B test.
• Provides insightful and scientifically-backed predictions.
• Outlines a clear picture for the A/B test, thus leading to more accurate outcomes.

How to Use it with A/B Testing?

To use the Automated Hypothesis Creator with A/B testing, follow these simple steps:

• Begin by clearly formulating your query.
• Use the text area in the tool to provide the necessary input data.
• Click the “Create Hypothesis” button.
• Wait for a while for the tool to process your request and generate a hypothesis.
• Once the hypothesis is created, use it as a basis for your A/B test.

Try other free tools:

• A/B Test Headline Generator
• Sample Size Calculator
• A/B Test Duration Calculator
• Statistical Significance Calculator

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• Open access
• Published: 09 July 2024

Automating psychological hypothesis generation with AI: when large language models meet causal graph

• Song Tong   ORCID: orcid.org/0000-0002-4183-8454 1 , 2 , 3 , 4   na1 ,
• Kai Mao 5   na1 ,
• Zhen Huang 2 ,
• Yukun Zhao 2 &
• Kaiping Peng 1 , 2 , 3 , 4  

Humanities and Social Sciences Communications volume  11 , Article number:  896 ( 2024 ) Cite this article

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Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on “well-being”, then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses ( t (59) = 3.34, p  = 0.007 and t (59) = 4.32, p  < 0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research.

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Introduction.

In an age in which the confluence of artificial intelligence (AI) with various subjects profoundly shapes sectors ranging from academic research to commercial enterprises, dissecting the interplay of these disciplines becomes paramount (Williams et al., 2023 ). In particular, psychology, which serves as a nexus between the humanities and natural sciences, consistently endeavors to demystify the complex web of human behaviors and cognition (Hergenhahn and Henley, 2013 ). Its profound insights have significantly enriched academia, inspiring innovative applications in AI design. For example, AI models have been molded on hierarchical brain structures (Cichy et al., 2016 ) and human attention systems (Vaswani et al., 2017 ). Additionally, these AI models reciprocally offer a rejuvenated perspective, deepening our understanding from the foundational cognitive taxonomy to nuanced esthetic perceptions (Battleday et al., 2020 ; Tong et al., 2021 ). Nevertheless, the multifaceted domain of psychology, particularly social psychology, has exhibited a measured evolution compared to its tech-centric counterparts. This can be attributed to its enduring reliance on conventional theory-driven methodologies (Henrich et al., 2010 ; Shah et al., 2015 ), a characteristic that stands in stark contrast to the burgeoning paradigms of AI and data-centric research (Bechmann and Bowker, 2019 ; Wang et al., 2023 ).

In the journey of psychological research, each exploration originates from a spark of innovative thought. These research trajectories may arise from established theoretical frameworks, daily event insights, anomalies within data, or intersections of interdisciplinary discoveries (Jaccard and Jacoby, 2019 ). Hypothesis generation is pivotal in psychology (Koehler, 1994 ; McGuire, 1973 ), as it facilitates the exploration of multifaceted influencers of human attitudes, actions, and beliefs. The HyGene model (Thomas et al., 2008 ) elucidated the intricacies of hypothesis generation, encompassing the constraints of working memory and the interplay between ambient and semantic memories. Recently, causal graphs have provided psychology with a systematic framework that enables researchers to construct and simulate intricate systems for a holistic view of “bio-psycho-social” interactions (Borsboom et al., 2021 ; Crielaard et al., 2022 ). Yet, the labor-intensive nature of the methodology poses challenges, which requires multidisciplinary expertise in algorithmic development, exacerbating the complexities (Crielaard et al., 2022 ). Meanwhile, advancements in AI, exemplified by models such as the generative pretrained transformer (GPT), present new avenues for creativity and hypothesis generation (Wang et al., 2023 ).

Building on this, notably large language models (LLMs) such as GPT-3, GPT-4, and Claude-2, which demonstrate profound capabilities to comprehend and infer causality from natural language texts, a promising path has emerged to extract causal knowledge from vast textual data (Binz and Schulz, 2023 ; Gu et al., 2023 ). Exciting possibilities are seen in specific scenarios in which LLMs and causal graphs manifest complementary strengths (Pan et al., 2023 ). Their synergistic combination converges human analytical and systemic thinking, echoing the holistic versus analytic cognition delineated in social psychology (Nisbett et al., 2001 ). This amalgamation enables fine-grained semantic analysis and conceptual understanding via LLMs, while causal graphs offer a global perspective on causality, alleviating the interpretability challenges of AI (Pan et al., 2023 ). This integrated methodology efficiently counters the inherent limitations of working and semantic memories in hypothesis generation and, as previous academic endeavors indicate, has proven efficacious across disciplines. For example, a groundbreaking study in physics synthesized 750,000 physics publications, utilizing cutting-edge natural language processing to extract 6368 pivotal quantum physics concepts, culminating in a semantic network forecasting research trajectories (Krenn and Zeilinger, 2020 ). Additionally, by integrating knowledge-based causal graphs into the foundation of the LLM, the LLM’s capability for causative inference significantly improves (Kıcıman et al., 2023 ).

To this end, our study seeks to build a pioneering analytical framework, combining the semantic and conceptual extraction proficiency of LLMs with the systemic thinking of the causal graph, with the aim of crafting a comprehensive causal network of semantic concepts within psychology. We meticulously analyzed 43,312 psychological articles, devising an automated method to construct a causal graph, and systematically mining causative concepts and their interconnections. Specifically, the initial sifting and preparation of the data ensures a high-quality corpus, and is followed by employing advanced extraction techniques to identify standardized causal concepts. This results in a graph database that serves as a reservoir of causal knowledge. In conclusion, using node embedding and similarity-based link prediction, we unearthed potential causal relationships, and thus generated the corresponding hypotheses.

To gauge the pragmatic value of our network, we selected 130 hypotheses on “well-being” generated by our framework, comparing them with hypotheses crafted by novice experts (doctoral students in psychology) and the LLM models. The results are encouraging: Our algorithm matches the caliber of novice experts, outshining the hypotheses generated solely by the LLM models in novelty. Additionally, through deep semantic analysis, we demonstrated that our algorithm contains more profound conceptual incorporations and a broader semantic spectrum.

Our study advances the field of psychology in two significant ways. Firstly, it extracts invaluable causal knowledge from the literature and converts it to visual graphics. These aids can feed algorithms to help deduce more latent causal relations and guide models in generating a plethora of novel causal hypotheses. Secondly, our study furnishes novel tools and methodologies for causal analysis and scientific knowledge discovery, representing the seamless fusion of modern AI with traditional research methodologies. This integration serves as a bridge between conventional theory-driven methodologies in psychology and the emerging paradigms of data-centric research, thereby enriching our understanding of the factors influencing psychology, especially within the realm of social psychology.

Methodological framework for hypothesis generation

The proposed LLM-based causal graph (LLMCG) framework encompasses three steps: literature retrieval, causal pair extraction, and hypothesis generation, as illustrated in Fig. 1 . In the literature gathering phase, ~140k psychology-related articles were downloaded from public databases. In step two, GPT-4 were used to distil causal relationships from these articles, culminating in the creation of a causal relationship network based on 43,312 selected articles. In the third step, an in-depth examination of these data was executed, adopting link prediction algorithms to forecast the dynamics within the causal relationship network for searching the highly potential causality concept pairs.

Note: LLM stands for large language model; LLMCG algorithm stands for LLM-based causal graph algorithm, which includes the processes of literature retrieval, causal pair extraction, and hypothesis generation.

Step 1: Literature retrieval

The primary data source for this study was a public repository of scientific articles, the PMC Open Access Subset. Our decision to utilize this repository was informed by several key attributes that it possesses. The PMC Open Access Subset boasts an expansive collection of over 2 million full-text XML science and medical articles, providing a substantial and diverse base from which to derive insights for our research. Furthermore, the open-access nature of the articles not only enhances the transparency and reproducibility of our methodology, but also ensures that the results and processes can be independently accessed and verified by other researchers. Notably, the content within this subset originates from recognized journals, all of which have undergone rigorous peer review, lending credence to the quality and reliability of the data we leveraged. Finally, an added advantage was the rich metadata accompanying each article. These metadata were instrumental in refining our article selection process, ensuring coherent thematic alignment with our research objectives in the domains of psychology.

To identify articles relevant to our study, we applied a series of filtering criteria. First, the presence of certain keywords within article titles or abstracts was mandatory. Some examples of these keywords include “psychol”, “clin psychol”, and “biol psychol”. Second, we exploited the metadata accompanying each article. The classification of articles based on these metadata ensured alignment with recognized thematic standards in the domains of psychology and neuroscience. Upon the application of these criteria, we managed to curate a subset of approximately 140K articles that most likely discuss causal concepts in both psychology and neuroscience.

Step 2: Causal pair extraction

The process of extracting causal knowledge from vast troves of scientific literature is intricate and multifaceted. Our methodology distils this complex process into four coherent steps, each serving a distinct purpose. (1) Article selection and cost analysis: Determines the feasibility of processing a specific volume of articles, ensuring optimal resource allocation. (2) Text extraction and analysis: Ensures the purity of the data that enter our causal extraction phase by filtering out nonrelevant content. (3) Causal knowledge extraction: Uses advanced language models to detect, classify, and standardize causal factors relationships present in texts. (4) Graph database storage: Facilitates structured storage, easy retrieval, and the possibility of advanced relational analyses for future research. This streamlined approach ensures accuracy, consistency, and scalability in our endeavor to understand the interplay of causal concepts in psychology and neuroscience.

Text extraction and cleaning

After a meticulous cost analysis detailed in Appendix A , our selection process identified 43,312 articles. This selection was strategically based on the criterion that the journal titles must incorporate the term “Psychol”, signifying their direct relevance to the field of psychology. The distributions of publication sources and years can be found in Table 1 . Extracting the full texts of the articles from their PDF sources was an essential initial step, and, for this purpose, the PyPDF2 Python library was used. This library allowed us to seamlessly extract and concatenate titles, abstracts, and main content from each PDF article. However, a challenge arose with the presence of extraneous sections such as references or tables, in the extracted texts. The implemented procedure, employing regular expressions in Python, was not only adept at identifying variations of the term “references” but also ascertained whether this section appeared as an isolated segment. This check was critical to ensure that the identified that the “references” section was indeed distinct, marking the start of a reference list without continuation into other text. Once identified as a standalone entity, the next step in the method was to efficiently remove the reference section and its subsequent content.

Causal knowledge extraction method

In our effort to extract causal knowledge, the choice of GPT-4 was not arbitrary. While several models were available for such tasks, GPT-4 emerged as a frontrunner due to its advanced capabilities (Wu et al., 2023 ), extensive training on diverse data, with its proven proficiency in understanding context, especially in complex scientific texts (Cheng et al., 2023 ; Sanderson, 2023 ). Other models were indeed considered; however, the capacity of GPT-4 to generate coherent, contextually relevant responses gave our project an edge in its specific requirements.

The extraction process commenced with the segmentation of the articles. Due to the token constraints inherent to GPT-4, it was imperative to break down the articles into manageable chunks, specifically those of 4000 tokens or fewer. This approach ensured a comprehensive interpretation of the content without omitting any potential causal relationships. The next phase was prompt engineering. To effectively guide the extraction capabilities of GPT-4, we crafted explicit prompts. A testament to this meticulous engineering is demonstrated in a directive in which we asked the model to elucidate causal pairs in a predetermined JSON format. For a clearer understanding, readers are referred to Table 2 , which elucidates the example prompt and the subsequent model response. After extraction, the outputs were not immediately cataloged. A filtering process was initiated to ascertain the standardization of the concept pairs. This process weeded out suboptimal outputs. Aiding in this quality control, GPT-4 played a pivotal role in the verification of causal pairs, determining their relevance, causality, and ensuring correct directionality. Finally, while extracting knowledge, we were aware of the constraints imposed by the GPT-4 API. There was a conscious effort to ensure that we operated within the bounds of 60 requests and 150k tokens per minute. This interplay of prompt engineering and stringent filtering was productive.

In addition, we conducted an exploratory study to assess GPT-4’s discernment between “causality” and “correlation” involved four graduate students (mean age 31 ± 10.23), each evaluating relationship pairs extracted from their familiar psychology articles. The experimental details and results can be found in Appendix A and Table A1. The results showed that out of 289 relationships identified by GPT-4, 87.54% were validated. Notably, when GPT-4 classified relationships as causal, only 13.02% (31/238) were recognized as non-relationship, while 65.55% (156/238) agreed upon as causality. This shows that GPT-4 can accurately extract relationships (causality or correlation) in psychological texts, underscoring the potential as a tool for the construction of causal graphs.

To enhance the robustness of the extracted causal relationships and minimize biases, we adopted a multifaceted approach. Recognizing the indispensable role of human judgment, we periodically subjected random samples of extracted causal relationships to the scrutiny of domain experts. Their valuable feedback was instrumental in the real-time fine-tuning the extraction process. Instead of heavily relying on referenced hypotheses, our focus was on extracting causal pairs, primarily from the findings mentioned in the main texts. This systematic methodology ultimately resulted in a refined text corpus distilled from 43,312 articles, which contained many conceptual insights and were primed for rigorous causal extraction.

Graph database storage

Our decision to employ Neo4j as the database system was strategic. Neo4j, as a graph database (Thomer and Wickett, 2020 ), is inherently designed to capture and represent complex relationships between data points, an attribute that is essential for understanding intricate causal relationships. Beyond its technical prowess, Neo4j provides advantages such as scalability, resilience, and efficient querying capabilities (Webber, 2012 ). It is particularly adept at traversing interconnected data points, making it an excellent fit for our causal relationship analysis. The mined causal knowledge finds its abode in the Neo4j graph database. Each pair of causal concepts is represented as a node, with its directionality and interpretations stored as attributes. Relationships provide related concepts together. Storing the knowledge graph in Neo4j allows for the execution of the graph algorithms to analyze concept interconnectivity and reveal potential relationships.

The graph database contains 197k concepts and 235k connections. Table 3 encapsulates the core concepts and provides a vivid snapshot of the most recurring themes; helping us to understand the central topics that dominate the current psychological discourse. A comprehensive examination of the core concepts extracted from 43,312 psychological papers, several distinct patterns and focal areas emerged. In particular, there is a clear balance between health and illness in psychological research. The prominence of terms such as “depression”, “anxiety”, and “symptoms of depression magnifies the commitment in the discipline to understanding and addressing mental illnesses. However, juxtaposed against these are positive terms such as “life satisfaction” and “sense of happiness”, suggesting that psychology not only fixates on challenges but also delves deeply into the nuances of positivity and well-being. Furthermore, the significance given to concepts such as “life satisfaction”, “sense of happiness”, and “job satisfaction” underscores an increasing recognition of emotional well-being and job satisfaction as integral to overall mental health. Intertwining the realms of psychology and neuroscience, terms such as “microglial cell activation”, “cognitive impairment”, and “neurodegenerative changes” signal a growing interest in understanding the neural underpinnings of cognitive and psychological phenomena. In addition, the emphasis on “self-efficacy”, “positive emotions”, and “self-esteem” reflect the profound interest in understanding how self-perception and emotions influence human behavior and well-being. Concepts such as “age”, “resilience”, and “creativity” further expand the canvas, showcasing the eclectic and comprehensive nature of inquiries in the field of psychology.

Overall, this analysis paints a vivid picture of modern psychological research, illuminating its multidimensional approach. It demonstrates a discipline that is deeply engaged with both the challenges and triumphs of human existence, offering holistic insight into the human mind and its myriad complexities.

Step 3: Hypothesis generation using link prediction

In the quest to uncover novel causal relationships beyond direct extraction from texts, the technique of link prediction emerges as a pivotal methodology. It hinges on the premise of proposing potential causal ties between concepts that our knowledge graph does not explicitly connect. The process intricately weaves together vector embedding, similarity analysis, and probability-based ranking. Initially, concepts are transposed into a vector space using node2vec, which is valued for its ability to capture topological nuances. Here, every pair of unconnected concepts is assigned a similarity score, and pairs that do not meet a set benchmark are quickly discarded. As we dive deeper into the higher echelons of these scored pairs, the likelihood of their linkage is assessed using the Jaccard similarity of their neighboring concepts. Subsequently, these potential causal relationships are organized in descending order of their derived probabilities, and the elite pairs are selected.

An illustration of this approach is provided in the case highlighted in Figure A1. For instance, the behavioral inhibition system (BIS) exhibits ties to both the behavioral activation system (BAS) and the subsequent behavioral response of the BAS when encountering reward stimuli, termed the BAS reward response. Simultaneously, another concept, interference, finds itself bound to both the BAS and the BAS Reward Response. This configuration hints at a plausible link between the BIS and interference. Such highly probable causal pairs are not mere intellectual curiosity. They act as springboards, catalyzing the genesis of new experimental designs or research hypotheses ripe for empirical probing. In essence, this capability equips researchers with a cutting-edge instrument, empowering them to navigate the unexplored waters of the psychological and neurological domains.

Using pairs of highly probable causal concepts, we pushed GPT-4 to conjure novel causal hypotheses that bridge concepts. To further elucidate the process of this method, Table 4 provides some examples of hypotheses generated from the process. Such hypotheses, as exemplified in the last row, underscore the potential and power of our method for generating innovative causal propositions.

Hypotheses evaluation and results

In this section, we present an analysis focusing on quality in terms of novelty and usefulness of the hypotheses generated. According to existing literature, these dimensions are instrumental in encapsulating the essence of inventive ideas (Boden, 2009 ; McCarthy et al., 2018 ; Miron-Spektor and Beenen, 2015 ). These parameters have not only been quintessential for gauging creative concepts, but they have also been adopted to evaluate the caliber of research hypotheses (Dowling and Lucey, 2023 ; Krenn and Zeilinger, 2020 ; Oleinik, 2019 ). Specifically, we evaluate the quality of the hypotheses generated by the proposed LLMCG algorithm in relation to those generated by PhD students from an elite university who represent human junior experts, the LLM model, which represents advanced AI systems, and the research ideas refined by psychological researchers which represents cooperation between AI and humans.

The evaluation comprises three main stages. In the first stage, the hypotheses are generated by all contributors, including steps taken to ensure fairness and relevance for comparative analysis. In the second stage, the hypotheses from the first stage are independently and blindly reviewed by experts who represent the human academic community. These experts are asked to provide hypothesis ratings using a specially designed questionnaire to ensure statistical validity. The third stage delves deeper by transforming each research idea into the semantic space of a bidirectional encoder representation from transformers (BERT) (Lee et al., 2023 ), allowing us to intricately analyze the intrinsic reasons behind the rating disparities among the groups. This semantic mapping not only pinpoints the nuanced differences, but also provides potential insights into the cognitive constructs of each hypothesis.

Evaluation procedure

Selection of the focus area for hypothesis generation.

Selecting an appropriate focus area for hypothesis generation is crucial to ensure a balanced and insightful comparison of the hypothesis generation capacities between various contributors. In this study, our goal is to gauge the quality of hypotheses derived from four distinct contributors, with measures in place to mitigate potential confounding variables that might skew the results among groups (Rubin, 2005 ). Our choice of domain is informed by two pivotal criteria: the intricacy and subtlety of the subject matter and familiarity with the domain. It is essential that our chosen domain boasts sufficient complexity to prompt meaningful hypothesis generation and offer a robust assessment of both AI and human contributors” depth of understanding and creativity. Furthermore, while human contributors should be well-acquainted with the domain, their expertise need not match the vast corpus knowledge of the AI.

In terms of overarching human pursuits such as the search for happiness, positive psychology distinguishes itself by avoiding narrowly defined, individual-centric challenges (Seligman and Csikszentmihalyi, 2000 ). This alignment with our selection criteria is epitomized by well-being, a salient concept within positive psychology, as shown in Table 3 . Well-being, with its multidimensional essence that encompass emotional, psychological, and social facets, and its central stature in both research and practical applications of positive psychology (Diener et al., 2010 ; Fredrickson, 2001 ; Seligman and Csikszentmihalyi, 2000 ), becomes the linchpin of our evaluation. The growing importance of well-being in the current global context offers myriad novel avenues for hypothesis generation and theoretical advancement (Forgeard et al., 2011 ; Madill et al., 2022 ; Otu et al., 2020 ). Adding to our rationale, the Positive Psychology Research Center at Tsinghua University is a globally renowned hub for cutting-edge research in this domain. Leveraging this stature, we secured participation from specialized Ph.D. students, reinforcing positive psychology as the most fitting domain for our inquiry.

Hypotheses comparison

In our study, the generated psychological hypotheses were categorized into four distinct groups, consisting of two experimental groups and two control groups. The experimental groups encapsulate hypotheses generated by our algorithm, either through random selection or handpicking by experts from a pool of generated hypotheses. On the other hand, control groups comprise research ideas that were meticulously crafted by doctoral students with substantial academic expertise in the domains and hypotheses generated by representative LLMs. In the following, we elucidate the methodology and underlying rationale for each group:

LLMCG algorithm output (Random-selected LLMCG)

Following the requirement of generating hypotheses centred on well-being, the LLMCG algorithm crafted 130 unique hypotheses. These hypotheses were derived by LLMCG’s evaluation of the most likely causal relationships related to well-being that had not been previously documented in research literature datasets. From this refined pool, 30 research ideas were chosen at random for this experimental group. These hypotheses represent the algorithm’s ability to identify causal relationships and formulate pertinent hypotheses.

LLMCG expert-vetted hypotheses (Expert-selected LLMCG)

For this group, two seasoned psychological researchers, one male aged 47 and one female aged 46, in-depth expertise in the realm of Positive Psychology, conscientiously handpicked 30 of the most promising hypotheses from the refined pool, excluding those from the Random-selected LLMCG category. The selection criteria centered on a holistic understanding of both the novelty and practical relevance of each hypothesis. With an illustrious postdoctoral journey and a robust portfolio of publications in positive psychology to their names, they rigorously sifted through the hypotheses, pinpointing those that showcased a perfect confluence of originality and actionable insight. These hypotheses were meticulously appraised for their relevance, structural coherence, and potential academic value, representing the nexus of machine intelligence and seasoned human discernment.

PhD students’ output (Control-Human)

We enlisted the expertise of 16 doctoral students from the Positive Psychology Research Center at Tsinghua University. Under the guidance of their supervisor, each student was provided with a questionnaire geared toward research on well-being. The participants were given a period of four working days to complete and return the questionnaire, which was distributed during vacation to ensure minimal external disruptions and commitments. The specific instructions provided in the questionnaire is detailed in Table B1 , and each participant was asked to complete 3–4 research hypotheses. By the stipulated deadline, we received responses from 13 doctoral students, with a mean age of 31.92 years (SD = 7.75 years), cumulatively presenting 41 hypotheses related to well-being. To maintain uniformity with the other groups, a random selection was made to shortlist 30 hypotheses for further analysis. These hypotheses reflect the integration of core theoretical concepts with the latest insights into the domain, presenting an academic interpretation rooted in their rigorous training and education. Including this group in our study not only provides a natural benchmark for human ingenuity and expertise but also underscores the invaluable contribution of human cognition in research ideation, serving as a pivotal contrast to AI-generated hypotheses. This juxtaposition illuminates the nuanced differences between human intellectual depth and AI’s analytical progress, enriching the comparative dimensions of our study.

Claude model output (Control-Claude)

This group exemplifies the pinnacle of current LLM technology in generating research hypotheses. Since LLMCG is a nascent technology, its assessment requires a comparative study with well-established counterparts, creating a key paradigm in comparative research. Currently, Claude-2 and GPT-4 represent the apex of AI technology. For example, Claude-2, with an accuracy rate of 54. 4% excels in reasoning and answering questions, substantially outperforming other models such as Falcon, Koala and Vicuna, which have accuracy rates of 17.1–25.5% (Wu et al., 2023 ). To facilitate a more comprehensive evaluation of the new model by researchers and to increase the diversity and breadth of comparison, we chose Claude-2 as the control model. Using the detailed instructions provided in Table B2, Claude-2 was iteratively prompted to generate research hypotheses, generating ten hypotheses per prompt, culminating in a total of 50 hypotheses. Although the sheer number and range of these hypotheses accentuate the capabilities of Claude-2, to ensure compatibility in terms of complexity and depth between all groups, a subsequent refinement was considered essential. With minimal human intervention, GPT-4 was used to evaluate these 50 hypotheses and select the top 30 that exhibited the most innovative, relevant, and academically valuable insights. This process ensured the infusion of both the LLM”s analytical prowess and a layer of qualitative rigor, thus giving rise to a set of hypotheses that not only align with the overarching theme of well-being but also resonate with current academic discourse.

Hypotheses assessment

The assessment of the hypotheses encompasses two key components: the evaluation conducted by eminent psychology professors emphasizing novelty and utility, and the deep semantic analysis involving BERT and t -distributed stochastic neighbor embedding ( t -SNE) visualization to discern semantic structures and disparities among hypotheses.

Human academic community

The review task was entrusted to three eminent psychology professors (all male, mean age = 42.33), who have a decade-long legacy in guiding doctoral and master”s students in positive psychology and editorial stints in renowned journals; their task was to conduct a meticulous evaluation of the 120 hypotheses. Importantly, to ensure unbiased evaluation, the hypotheses were presented to them in a completely randomized order in the questionnaire.

Our emphasis was undeniably anchored to two primary tenets: novelty and utility (Cohen, 2017 ; Shardlow et al., 2018 ; Thompson and Skau, 2023 ; Yu et al., 2016 ), as shown in Table B3 . Utility in hypothesis crafting demands that our propositions extend beyond mere factual accuracy; they must resonate deeply with academic investigations, ensuring substantial practical implications. Given the inherent challenges of research, marked by constraints in time, manpower, and funding, it is essential to design hypotheses that optimize the utilization of these resources. On the novelty front, we strive to introduce innovative perspectives that have the power to challenge and expand upon existing academic theories. This not only propels the discipline forward but also ensures that we do not inadvertently tread on ground already covered by our contemporaries.

Deep semantic analysis

While human evaluations provide invaluable insight into the novelty and utility of hypotheses, to objectively discern and visualize semantic structures and the disparities among them, we turn to the realm of deep learning. Specifically, we employ the power of BERT (Devlin et al., 2018 ). BERT, as highlighted by Lee et al. ( 2023 ), had a remarkable potential to assess the innovation of ideas. By translating each hypothesis into a high-dimensional vector in the BERT domain, we obtain the profound semantic core of each statement. However, such granularity in dimensions presents challenges when aiming for visualization.

To alleviate this and to intuitively understand the clustering and dispersion of these hypotheses in semantic space, we deploy the t -SNE ( t -distributed Stochastic Neighbor Embedding) technique (Van der Maaten and Hinton, 2008 ), which is adept at reducing the dimensionality of the data while preserving the relative pairwise distances between the items. Thus, when we map our BERT-encoded hypotheses onto a 2D t -SNE plane, an immediate visual grasp on how closely or distantly related our hypotheses are in terms of their semantic content. Our intent is twofold: to understand the semantic terrains carved out by the different groups and to infer the potential reasons for some of the hypotheses garnered heightened novelty or utility ratings from experts. The convergence of human evaluations and semantic layouts, as delineated by Algorithm 1 in Appendix B , reveal the interplay between human intuition and the inherent semantic structure of the hypotheses.

Qualitative analysis by topic analysis

To better understand the underlying thought processes and the topical emphasis of both PhD students and the LLMCG model, qualitative analyses were performed using visual tools such as word clouds and connection graphs, as detailed in Appendix B . The word cloud, as a graphical representation, effectively captures the frequency and importance of terms, providing direct visualization of the dominant themes. Connection graphs, on the other hand, elucidate the relationships and interplay between various themes and concepts. Using these visual tools, we aimed to achieve a more intuitive and clear representation of the data, allowing for easy comparison and interpretation.

Observations drawn from both the word clouds and the connection graphs in Figures B1 and B2 provide us with a rich tapestry of insights into the thought processes and priorities of Ph.D. students and the LLMCG model. For instance, the emphasis in the Control-Human word cloud on terms such as “robot” and “AI” indicates a strong interest among Ph.D. students in the nexus between technology and psychology. It is particularly fascinating to see a group of academically trained individuals focusing on the real world implications and intersections of their studies, as shown by their apparent draw toward trending topics. This not only underscores their adaptability but also emphasizes the importance of contextual relevance. Conversely, the LLMCG groups, particularly the Expert-selected LLMCG group, emphasize the community, collective experiences, and the nuances of social interconnectedness. This denotes a deep-rooted understanding and application of higher-order social psychological concepts, reflecting the model”s ability to dive deep into the intricate layers of human social behavior.

Furthermore, the connection graphs support these observations. The Control-Human graph, with its exploration of themes such as “Robot Companionship” and its relation to factors such as “heart rate variability (HRV)”, demonstrates a confluence of technology and human well-being. The other groups, especially the Random-selected LLMCG group, yield themes that are more societal and structural, hinting at broader determinants of individual well-being.

Analysis of human evaluations

To quantify the agreement among the raters, we employed Spearman correlation coefficients. The results, as shown in Table B5, reveal a spectrum of agreement levels between the reviewer pairs, showcasing the subjective dimension intrinsic to the evaluation of novelty and usefulness. In particular, the correlation between reviewer 1 and reviewer 2 in novelty (Spearman r  = 0.387, p  < 0.0001) and between reviewer 2 and reviewer 3 in usefulness (Spearman r  = 0.376, p  < 0.0001) suggests a meaningful level of consensus, particularly highlighting their capacity to identify valuable insights when evaluating hypotheses.

The variations in correlation values, such as between reviewer 2 and reviewer 3 ( r  = 0.069, p  = 0.453), can be attributed to the diverse research orientations and backgrounds of each reviewer. Reviewer 1 focuses on social ecology, reviewer 3 specializes in neuroscientific methodologies, and reviewer 2 integrates various views using technologies like virtual reality, and computational methods. In our evaluation, we present specific hypotheses cases to illustrate the differing perspectives between reviewers, as detailed in Table B4 and Figure B3. For example, C5 introduces the novel concept of “Virtual Resilience”. Reviewers 1 and 3 highlighted its originality and utility, while reviewer 2 rated it lower in both categories. Meanwhile, C6, which focuses on social neuroscience, resonated with reviewer 3, while reviewers 1 and 2 only partially affirmed it. These differences underscore the complexity of evaluating scientific contributions and highlight the importance of considering a range of expert opinions for a comprehensive evaluation.

This assessment is divided into two main sections: Novelty analysis and usefulness analysis.

Novelty analysis

In the dynamic realm of scientific research, measuring and analyzing novelty is gaining paramount importance (Shin et al., 2022 ). ANOVA was used to analyze the novelty scores represented in Fig. 2 a, and we identified a significant influence of the group factor on the mean novelty score between different reviewers. Initially, z-scores were calculated for each reviewer”s ratings to standardize the scoring scale, which were then averaged. The distinct differences between the groups, as visualized in the boxplots, are statistically underpinned by the results in Table 5 . The ANOVA results revealed a pronounced effect of the grouping factor ( F (3116) = 6.92, p  = 0.0002), with variance explained by the grouping factor (R-squared) of 15.19%.

Box plots on the left ( a ) and ( b ) depict distributions of novelty and usefulness scores, respectively, while smoothed line plots on the right demonstrate the descending order of novelty and usefulness scores and subjected to a moving average with a window size of 2. * denotes p  < 0.05, ** denotes p  <0.01.

Further pairwise comparisons using the Bonferroni method, as delineated in Table 5 and visually corroborated by Fig. 2 a; significant disparities were discerned between Random-selected LLMCG and Control-Claude ( t (59) = 3.34, p  = 0.007) and between Control-Human and Control-Claude ( t (59) = 4.32, p  < 0.001). The Cohen’s d values of 0.8809 and 1.1192 respectively indicate that the novelty scores for the Random-selected LLMCG and Control-Human groups are significantly higher than those for the Control-Claude group. Additionally, when considering the cumulative distribution plots to the right of Fig. 2 a, we observe the distributional characteristics of the novel scores. For example, it can be observed that the Expert-selected LLMCG curve portrays a greater concentration in the middle score range when compared to the Control-Claude , curve but dominates in the high novelty scores (highlighted in dashed rectangle). Moreover, comparisons involving Control-Human with both Random-selected LLMCG and Expert-selected LLMCG did not manifest statistically significant variances, indicating aligned novelty perceptions among these groups. Finally, the comparisons between Expert-selected LLMCG and Control-Claude ( t (59) = 2.49, p  = 0.085) suggest a trend toward significance, with a Cohen’s d value of 0.6226 indicating generally higher novelty scores for Expert-selected LLMCG compared to Control-Claude .

To mitigate potential biases due to individual reviewer inclinations, we expanded our evaluation to include both median and maximum z-scores from the three reviewers for each hypothesis. These multifaceted analyses enhance the robustness of our results by minimizing the influence of extreme values and potential outliers. First, when analyzing the median novelty scores, the ANOVA test demonstrated a notable association with the grouping factor ( F (3,116) = 6.54, p  = 0.0004), which explained 14.41% of the variance. As illustrated in Table 5 , pairwise evaluations revealed significant disparities between Control-Human and Control-Claude ( t (59) = 4.01, p  = 0.001), with Control-Human performing significantly higher than Control-Claude (Cohen’s d  = 1.1031). Similarly, there were significant differences between Random-selected LLMCG and Control-Claude ( t (59) = 3.40, p  = 0.006), where Random-selected LLMCG also significantly outperformed Control-Claude (Cohen’s d  = 0.8875). Interestingly, the comparison of Expert-selected LLMCG with Control-Claude ( t (59) = 1.70, p  = 0.550) and other group pairings did not include statistically significant differences.

Subsequently, turning our attention to maximum novelty scores provided crucial insights, especially where outlier scores may carry significant weight. The influence of the grouping factor was evident ( F (3,116) = 7.20, p  = 0.0002), indicating an explained variance of 15.70%. In particular, clear differences emerged between Control-Human and Control-Claude ( t (59) = 4.36, p  < 0.001), and between Random-selected LLMCG and Control-Claude ( t (59) = 3.47, p  = 0.004). A particularly intriguing observation was the significant difference between Expert-selected LLMCG and Control-Claude ( t (59) = 3.12, p  = 0.014). The Cohen’s d values of 1.1637, 1.0457, and 0.6987 respectively indicate that the novelty scores for the Control-Human , Random-selected LLMCG , and Expert-selected LLMCG groups are significantly higher than those for the Control-Claude group. Together, these analyses offer a multifaceted perspective on novelty evaluations. Specifically, the results of the median analysis echo and support those of the mean, reinforcing the reliability of our assessments. The discerned significance between Control-Claude and Expert-selected LLMCG in the median data emphasizes the intricate differences, while also pointing to broader congruence in novelty perceptions.

Usefulness analysis

Evaluating the practical impact of hypotheses is crucial in scientific research assessments. In the mean useful spectrum, the grouping factor did not exert a significant influence ( F (3,116) = 5.25, p  = 0.553). Figure 2 b presents the utility score distributions between groups. The narrow interquartile range of Control-Human suggests a relatively consistent assessment among reviewers. On the other hand, the spread and outliers in the Control-Claude distribution hint at varied utility perceptions. Both LLMCG groups cover a broad score range, demonstrating a mixture of high and low utility scores, while the Expert-selected LLMCG gravitates more toward higher usefulness scores. The smoothed line plots accompanying Fig. 2 b further detail the score densities. For instance, Random-selected LLMCG boasts several high utility scores, counterbalanced by a smattering of low scores. Interestingly, the distributions for Control-Human and Expert-selected LLMCG appear to be closely aligned. While mean utility scores provide an overarching view, the nuances within the boxplots and smoothed plots offer deeper insights. This comprehensive understanding can guide future endeavors in content generation and evaluation, spotlighting key areas of focus and potential improvements.

Comparison between the LLMCG and GPT-4

To evaluate the impact of integrating a causal graph with GPT-4, we performed an ablation study comparing the hypotheses generated by GPT-4 alone and those of the proposed LLMCG framework. For this experiment, 60 hypotheses were created using GPT-4, following the detailed instructions in Table B2 . Furthermore, 60 hypotheses for the LLMCG group were randomly selected from the remaining pool of 70 hypotheses. Subsequently, both sets of hypotheses were assessed by three independent reviewers for novelty and usefulness, as previously described.

Table 6 shows a comparison between the GPT-4 and LLMCG groups, highlighting a significant difference in novelty scores (mean value: t (119) = 6.60, p  < 0.0001) but not in usefulness scores (mean value: t (119) = 1.31, p  = 0.1937). This indicates that the LLMCG framework significantly enhances hypothesis novelty (all Cohen’s d  > 1.1) without affecting usefulness compared to the GPT-4 group. Figure B6 visually contrasts these findings, underlining the causal graph’s unique role in fostering novel hypothesis generation when integrated with GPT-4.

The t -SNE visualizations (Fig. 3 ) illustrate the semantic relationships between different groups, capturing the patterns of novelty and usefulness. Notably, a distinct clustering among PhD students suggests shared academic influences, while the LLMCG groups display broader topic dispersion, hinting at a wider semantic understanding. The size of the bubbles reflects the novelty and usefulness scores, emphasizing the diverse perceptions of what is considered innovative versus beneficial. Additionally, the numbers near the yellow dots represent the participant IDs, which demonstrated that the semantics of the same participant, such as H05 or H06, are closely aligned. In Fig. B4 , a distinct clustering of examples is observed, particularly highlighting the close proximity of hypotheses C3, C4, and C8 within the semantic space. This observation is further elucidated in Appendix B , enhancing the comprehension of BERT’s semantic representation. Instead of solely depending on superficial textual descriptions, this analysis penetrates into the underlying understanding of concepts within the semantic space, a topic also explored in recent research (Johnson et al., 2023 ).

Comparison of ( a ) novelty and ( b ) usefulness scores (bubble size scaled by 100) among the different groups.

In the distribution of semantic distances (Fig. 4 ), we observed that the Control-Human group exhibits a distinctively greater semantic distance in comparison to the other groups, emphasizing their unique semantic orientations. The statistical support for this observation is derived from the ANOVA results, with a significant F-statistic ( F (3,1652) = 84.1611, p  < 0.00001), underscoring the impact of the grouping factor. This factor explains a remarkable 86.96% of the variance, as indicated by the R -squared value. Multiple comparisons, as shown in Table 7 , further elucidate the subtleties of these group differences. Control-Human and Control-Claude exhibit a significant contrast in their semantic distances, as highlighted by the t value of 16.41 and the adjusted p value ( < 0.0001). This difference indicates distinct thought patterns or emphasis in the two groups. Notably, Control-Human demonstrates a greater semantic distance (Cohen’s d = 1.1630). Similarly, a comparison of the Control-Claude and LLMCG models reveals pronounced differences (Cohen’s d  > 0.9), more so with the Expert-selected LLMCG ( p  < 0.0001). A comparison of Control-Human with the LLMCG models shows divergent semantic orientations, with statistically significant larger distances than Random-selected LLMCG ( p  = 0.0036) and a trend toward difference with Expert-selected LLMCG ( p  = 0.0687). Intriguingly, the two LLMCG groups—Random-selected and Expert-selected—exhibit similar semantic distances, as evidenced by a nonsignificant p value of 0.4362. Furthermore, the significant distinctions we observed, particularly between the Control-Human and other groups, align with human evaluations of novelty. This coherence indicates that the BERT space representation coupled with statistical analyses could effectively mimic human judgment. Such results underscore the potential of this approach for automated hypothesis testing, paving the way for more efficient and streamlined semantic evaluations in the future.

Note: ** denotes p  < 0.01, **** denotes p  < 0.0001.

In general, visual and statistical analyses reveal the nuanced semantic landscapes of each group. While the Ph.D. students’ shared background influences their clustering, the machine models exhibit a comprehensive grasp of topics, emphasizing the intricate interplay of individual experiences, academic influences, and algorithmic understanding in shaping semantic representations.

This investigation carried out a detailed evaluation of the various hypothesis contributors, blending both quantitative and qualitative analyses. In terms of topic analysis, distinct variations were observed between Control-Human and LLMCG, the latter presenting more expansive thematic coverage. For human evaluation, hypotheses from Ph.D. students paralleled the LLMCG in novelty, reinforcing AI’s growing competence in mirroring human innovative thinking. Furthermore, when juxtaposed with AI models such as Control-Claude , the LLMCG exhibited increased novelty. Deep semantic analysis via t -SNE and BERT representations allowed us to intuitively grasp semantic essence of hypotheses, signaling the possibility of future automated hypothesis assessments. Interestingly, LLMCG appeared to encompass broader complementary domains compared to human input. Taken together, these findings highlight the emerging role of AI in hypothesis generation and provide key insights into hypothesis evaluation across diverse origins.

General discussion

This research delves into the synergistic relationship between LLM and causal graphs in the hypothesis generation process. Our findings underscore the ability of LLM, when integrated with causal graph techniques, to produce meaningful hypotheses with increased efficiency and quality. By centering our investigation on “well-being” we emphasize its pivotal role in psychological studies and highlight the potential convergence of technology and society. A multifaceted assessment approach to evaluate quality by topic analysis, human evaluation and deep semantic analysis demonstrates that AI-augmented methods not only outshine LLM-only techniques in generating hypotheses with superior novelty and show quality on par with human expertise but also boast the capability for more profound conceptual incorporations and a broader semantic spectrum. Such a multifaceted lens of assessment introduces a novel perspective for the scholarly realm, equipping researchers with an enriched understanding and an innovative toolset for hypothesis generation. At its core, the melding of LLM and causal graphs signals a promising frontier, especially in regard to dissecting cornerstone psychological constructs such as “well-being”. This marriage of methodologies, enriched by the comprehensive assessment angle, deepens our comprehension of both the immediate and broader ramifications of our research endeavors.

The prominence of causal graphs in psychology is profound, they offer researchers a unified platform for synthesizing and hypothesizing diverse psychological realms (Borsboom et al., 2021 ; Uleman et al., 2021 ). Our study echoes this, producing groundbreaking hypotheses comparable in depth to early expert propositions. Deep semantic analysis bolstered these findings, emphasizing that our hypotheses have distinct cross-disciplinary merits, particularly when compared to those of individual doctoral scholars. However, the traditional use of causal graphs in psychology presents challenges due to its demanding nature, often requiring insights from multiple experts (Crielaard et al., 2022 ). Our research, however, harnesses LLM’s causal extraction, automating causal pair derivation and, in turn, minimizing the need for extensive expert input. The union of the causal graphs’ systematic approach with AI-driven creativity, as seen with LLMs, paves the way for the future of psychological inquiry. Thanks to advancements in AI, barriers once created by causal graphs’ intricate procedures are being dismantled. Furthermore, as the era of big data dawns, the integration of AI and causal graphs in psychology augments research capabilities, but also brings into focus the broader implications for society. This fusion provides a nuanced understanding of the intricate sociopsychological dynamics, emphasizing the importance of adapting research methodologies in tandem with technological progress.

In the realm of research, LLMs serve a unique purpose, often by acting as the foundation or baseline against which newer methods and approaches are assessed. The demonstrated productivity enhancements by generative AI tools, as evidenced by Noy and Zhang ( 2023 ), indicate the potential of such LLMs. In our investigation, we pit the hypotheses generated by such substantial models against our integrated LLMCG approach. Intriguingly, while these LLMs showcased admirable practicality in their hypotheses, they substantially lagged behind in terms of innovation when juxtaposed with the doctoral student and LLMCG group. This divergence in results can be attributed to the causal network curated from 43k research papers, funneling the vast knowledge reservoir of the LLM squarely into the realm of scientific psychology. The increased precision in hypothesis generation by these models fits well within the framework of generative networks. Tong et al. ( 2021 ) highlighted that, by integrating structured constraints, conventional neural networks can accurately generate semantically relevant content. One of the salient merits of the causal graph, in this context, is its ability to alleviate inherent ambiguity or interpretability challenges posed by LLMs. By providing a systematic and structured framework, the causal graph aids in unearthing the underlying logic and rationale of the outputs generated by LLMs. Notably, this finding echoes the perspective of Pan et al. ( 2023 ), where the integration of structured knowledge from knowledge graphs was shown to provide an invaluable layer of clarity and interpretability to LLMs, especially in complex reasoning tasks. Such structured approaches not only boost the confidence of researchers in the hypotheses derived but also augment the transparency and understandability of LLM outputs. In essence, leveraging causal graphs may very well herald a new era in model interpretability, serving as a conduit to unlock the black box that large models often represent in contemporary research.

In the ever-evolving tapestry of research, every advancement invariably comes with its unique set of constraints, and our study was no exception. On the technical front, a pivotal challenge stemmed from the opaque inner workings of the GPT. Determining the exact machinations within the GPT that lead to the formation of specific causal pairs remains elusive, thereby reintroducing the age-old issue of AI’s inherent lack of transparency (Buruk, 2023 ; Cao and Yousefzadeh, 2023 ). This opacity is magnified in our sparse causal graph, which, while expansive, is occasionally riddled with concepts that, while semantically distinct, converge in meaning. In tangible applications, a careful and meticulous algorithmic evaluation would be imperative to construct an accurate psychological conceptual landscape. Delving into psychology, which bridges humanities and natural sciences, it continuously aims to unravel human cognition and behavior (Hergenhahn and Henley, 2013 ). Despite the dominance of traditional methodologies (Henrich et al., 2010 ; Shah et al., 2015 ), the present data-centric era amplifies the synergy of technology and humanities, resonating with Hasok Chang’s vision of enriched science (Chang, 2007 ). This symbiosis is evident when assessing structural holes in social networks (Burt, 2004 ) and viewing novelty as a bridge across these divides (Foster et al., 2021 ). Such perspectives emphasize the importance of thorough algorithmic assessments, highlighting potential avenues in humanities research, especially when incorporating large language models for innovative hypothesis crafting and verification.

However, there are some limitations to this research. Firstly, we acknowledge that constructing causal relationship graphs has potential inaccuracies, with ~13% relationship pairs not aligning with human expert estimations. Enhancing the estimation of relationship extraction could be a pathway to improve the accuracy of the causal graph, potentially leading to more robust hypotheses. Secondly, our validation process was limited to 130 hypotheses, however, the vastness of our conceptual landscape suggests countless possibilities. As an exemplar, the twenty pivotal psychological concepts highlighted in Table 3 alone could spawn an extensive array of hypotheses. However, the validation of these surrounding hypotheses would unquestionably lead to a multitude of speculations. A striking observation during our validation was the inconsistency in the evaluations of the senior expert panels (as shown in Table B5 ). This shift underscores a pivotal insight: our integration of AI has transitioned the dependency on scarce expert resources from hypothesis generation to evaluation. In the future, rigorous evaluations ensuring both novelty and utility could become a focal point of exploration. The promising path forward necessitates a thoughtful integration of technological innovation and human expertise to fully realize the potential suggested by our study.

In conclusion, our research provides pioneering insight into the symbiotic fusion of LLMs, which are epitomized by GPT, and causal graphs from the realm of psychological hypothesis generation, especially emphasizing “well-being”. Importantly, as highlighted by (Cao and Yousefzadeh, 2023 ), ensuring a synergistic alignment between domain knowledge and AI extrapolation is crucial. This synergy serves as the foundation for maintaining AI models within their conceptual limits, thus bolstering the validity and reliability of the hypotheses generated. Our approach intricately interweaves the advanced capabilities of LLMs with the methodological prowess of causal graphs, thereby optimizing while also refining the depth and precision of hypothesis generation. The causal graph, of paramount importance in psychology due to its cross-disciplinary potential, often demands vast amounts of expert involvement. Our innovative approach addresses this by utilizing LLM’s exceptional causal extraction abilities, effectively facilitating the transition of intense expert engagement from hypothesis creation to evaluation. Therefore, our methodology combined LLM with causal graphs, propelling psychological research forward by improving hypothesis generation and offering tools to blend theoretical and data-centric approaches. This synergy particularly enriches our understanding of social psychology’s complex dynamics, such as happiness research, demonstrating the profound impact of integrating AI with traditional research frameworks.

Data availability

The data generated and analyzed in this study are partially available within the Supplementary materials . For additional data supporting the findings of this research, interested parties may contact the corresponding author, who will provide the information upon receiving a reasonable request.

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Acknowledgements

The authors thank Dr. Honghong Bai (Radboud University), Dr. ChienTe Wu (The University of Tokyo), Dr. Peng Cheng (Tsinghua University), and Yusong Guo (Tsinghua University) for their great comments on the earlier version of this manuscript. This research has been generously funded by personal contributions, with special acknowledgment to K. Mao. Additionally, he conceived and developed the causality graph and AI hypothesis generation technology presented in this paper from scratch, and generated all AI hypotheses and paid for its costs. The authors sincerely thank K. Mao for his support, which enabled this research. In addition, K. Peng and S. Tong were partly supported by the Tsinghua University lnitiative Scientific Research Program (No. 20213080008), Self-Funded Project of Institute for Global Industry, Tsinghua University (202-296-001), Shuimu Scholars program of Tsinghua University (No. 2021SM157), and the China Postdoctoral International Exchange Program (No. YJ20210266).

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Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China

Song Tong & Kaiping Peng

Positive Psychology Research Center, School of Social Sciences, Tsinghua University, Beijing, China

Song Tong, Zhen Huang, Yukun Zhao & Kaiping Peng

AI for Wellbeing Lab, Tsinghua University, Beijing, China

Institute for Global Industry, Tsinghua University, Beijing, China

Kindom KK, Tokyo, Japan

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Song Tong: Data analysis, Experiments, Writing—original draft & review. Kai Mao: Designed the causality graph methodology, Generated AI hypotheses, Developed hypothesis generation techniques, Writing—review & editing. Zhen Huang: Statistical Analysis, Experiments, Writing—review & editing. Yukun Zhao: Conceptualization, Project administration, Supervision, Writing—review & editing. Kaiping Peng: Conceptualization, Writing—review & editing.

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Tong, S., Mao, K., Huang, Z. et al. Automating psychological hypothesis generation with AI: when large language models meet causal graph. Humanit Soc Sci Commun 11 , 896 (2024). https://doi.org/10.1057/s41599-024-03407-5

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Demystifying Hypothesis Generation: A Guide to AI-Driven Insights

Hypothesis generation involves making informed guesses about various aspects of a business, market, or problem that need further exploration and testing. This article discusses the process you need to follow while generating hypothesis and how an AI tool, like Akaike's BYOB can help you achieve the process quicker and better.

What is Hypothesis Generation?

Hypothesis generation involves making informed guesses about various aspects of a business, market, or problem that need further exploration and testing. It's a crucial step while applying the scientific method to business analysis and decision-making. 

Here is an example from a popular B-school marketing case study: 

A bicycle manufacturer noticed that their sales had dropped significantly in 2002 compared to the previous year. The team investigating the reasons for this had many hypotheses. One of them was: “many cycling enthusiasts have switched to walking with their iPods plugged in.” The Apple iPod was launched in late 2001 and was an immediate hit among young consumers. Data collected manually by the team seemed to show that the geographies around Apple stores had indeed shown a sales decline.

Traditionally, hypothesis generation is time-consuming and labour-intensive. However, the advent of Large Language Models (LLMs) and Generative AI (GenAI) tools has transformed the practice altogether. These AI tools can rapidly process extensive datasets, quickly identifying patterns, correlations, and insights that might have even slipped human eyes, thus streamlining the stages of hypothesis generation.

These tools have also revolutionised experimentation by optimising test designs, reducing resource-intensive processes, and delivering faster results. LLMs' role in hypothesis generation goes beyond mere assistance, bringing innovation and easy, data-driven decision-making to businesses.

Hypotheses come in various types, such as simple, complex, null, alternative, logical, statistical, or empirical. These categories are defined based on the relationships between the variables involved and the type of evidence required for testing them. In this article, we aim to demystify hypothesis generation. We will explore the role of LLMs in this process and outline the general steps involved, highlighting why it is a valuable tool in your arsenal.

Understanding Hypothesis Generation

A hypothesis is born from a set of underlying assumptions and a prediction of how those assumptions are anticipated to unfold in a given context. Essentially, it's an educated, articulated guess that forms the basis for action and outcome assessment.

A hypothesis is a declarative statement that has not yet been proven true. Based on past scholarship , we could sum it up as the following: 

• A definite statement, not a question
• Based on observations and knowledge
• Testable and can be proven wrong
• Predicts the anticipated results clearly
• Contains a dependent and an independent variable where the dependent variable is the phenomenon being explained and the independent variable does the explaining

In a business setting, hypothesis generation becomes essential when people are made to explain their assumptions. This clarity from hypothesis to expected outcome is crucial, as it allows people to acknowledge a failed hypothesis if it does not provide the intended result. Promoting such a culture of effective hypothesising can lead to more thoughtful actions and a deeper understanding of outcomes. Failures become just another step on the way to success, and success brings more success.

Hypothesis generation is a continuous process where you start with an educated guess and refine it as you gather more information. You form a hypothesis based on what you know or observe.

Say you're a pen maker whose sales are down. You look at what you know:

• I can see that pen sales for my brand are down in May and June.
• I also know that schools are closed in May and June and that schoolchildren use a lot of pens.
• I hypothesise that my sales are down because school children are not using pens in May and June, and thus not buying newer ones.

The next step is to collect and analyse data to test this hypothesis, like tracking sales before and after school vacations. As you gather more data and insights, your hypothesis may evolve. You might discover that your hypothesis only holds in certain markets but not others, leading to a more refined hypothesis.

Once your hypothesis is proven correct, there are many actions you may take - (a) reduce supply in these months (b) reduce the price so that sales pick up (c) release a limited supply of novelty pens, and so on.

Once you decide on your action, you will further monitor the data to see if your actions are working. This iterative cycle of formulating, testing, and refining hypotheses - and using insights in decision-making - is vital in making impactful decisions and solving complex problems in various fields, from business to scientific research.

How do Analysts generate Hypotheses? Why is it iterative?

A typical human working towards a hypothesis would start with:

    1. Picking the Default Action

    2. Determining the Alternative Action

    3. Figuring out the Null Hypothesis (H0)

    4. Inverting the Null Hypothesis to get the Alternate Hypothesis (H1)

    5. Hypothesis Testing

The default action is what you would naturally do, regardless of any hypothesis or in a case where you get no further information. The alternative action is the opposite of your default action.

The null hypothesis, or H0, is what brings about your default action. The alternative hypothesis (H1) is essentially the negation of H0.

For example, suppose you are tasked with analysing a highway tollgate data (timestamp, vehicle number, toll amount) to see if a raise in tollgate rates will increase revenue or cause a volume drop. Following the above steps, we can determine:

Now, we can start looking at past data of tollgate traffic in and around rate increases for different tollgates. Some data might be irrelevant. For example, some tollgates might be much cheaper so customers might not have cared about an increase. Or, some tollgates are next to a large city, and customers have no choice but to pay. 

Ultimately, you are looking for the level of significance between traffic and rates for comparable tollgates. Significance is often noted as its P-value or probability value . P-value is a way to measure how surprising your test results are, assuming that your H0 holds true.

The lower the p-value, the more convincing your data is to change your default action.

Usually, a p-value that is less than 0.05 is considered to be statistically significant, meaning there is a need to change your null hypothesis and reject your default action. In our example, a low p-value would suggest that a 10% increase in the toll rate causes a significant dip in traffic (>3%). Thus, it is better if we keep our rates as is if we want to maintain revenue. 

In other examples, where one has to explore the significance of different variables, we might find that some variables are not correlated at all. In general, hypothesis generation is an iterative process - you keep looking for data and keep considering whether that data convinces you to change your default action.

Internal and External Data 

Hypothesis generation feeds on data. Data can be internal or external. In businesses, internal data is produced by company owned systems (areas such as operations, maintenance, personnel, finance, etc). External data comes from outside the company (customer data, competitor data, and so on).

Let’s consider a real-life hypothesis generated from internal data: 

Multinational company Johnson & Johnson was looking to enhance employee performance and retention.  Initially, they favoured experienced industry candidates for recruitment, assuming they'd stay longer and contribute faster. However, HR and the people analytics team at J&J hypothesised that recent college graduates outlast experienced hires and perform equally well.  They compiled data on 47,000 employees to test the hypothesis and, based on it, Johnson & Johnson increased hires of new graduates by 20% , leading to reduced turnover with consistent performance. 

For an analyst (or an AI assistant), external data is often hard to source - it may not be available as organised datasets (or reports), or it may be expensive to acquire. Teams might have to collect new data from surveys, questionnaires, customer feedback and more. 

Further, there is the problem of context. Suppose an analyst is looking at the dynamic pricing of hotels offered on his company’s platform in a particular geography. Suppose further that the analyst has no context of the geography, the reasons people visit the locality, or of local alternatives; then the analyst will have to learn additional context to start making hypotheses to test. 

Internal data, of course, is internal, meaning access is already guaranteed. However, this probably adds up to staggering volumes of data. 

Looking Back, and Looking Forward

Data analysts often have to generate hypotheses retrospectively, where they formulate and evaluate H0 and H1 based on past data. For the sake of this article, let's call it retrospective hypothesis generation.

Alternatively, a prospective approach to hypothesis generation could be one where hypotheses are formulated before data collection or before a particular event or change is implemented. 

For example: 

A pen seller has a hypothesis that during the lean periods of summer, when schools are closed, a Buy One Get One (BOGO) campaign will lead to a 100% sales recovery because customers will buy pens in advance.  He then collects feedback from customers in the form of a survey and also implements a BOGO campaign in a single territory to see whether his hypothesis is correct, or not.

The HR head of a multi-office employer realises that some of the company’s offices have been providing snacks at 4:30 PM in the common area, and the rest have not. He has a hunch that these offices have higher productivity. The leader asks the company’s data science team to look at employee productivity data and the employee location data. “Am I correct, and to what extent?”, he asks. 

These examples also reflect another nuance, in which the data is collected differently: 

• Observational: Observational testing happens when researchers observe a sample population and collect data as it occurs without intervention. The data for the snacks vs productivity hypothesis was observational. 
• Experimental: In experimental testing, the sample is divided into multiple groups, with one control group. The test for the non-control groups will be varied to determine how the data collected differs from that of the control group. The data collected by the pen seller in the single territory experiment was experimental.

Such data-backed insights are a valuable resource for businesses because they allow for more informed decision-making, leading to the company's overall growth. Taking a data-driven decision, from forming a hypothesis to updating and validating it across iterations, to taking action based on your insights reduces guesswork, minimises risks, and guides businesses towards strategies that are more likely to succeed.

How can GenAI help in Hypothesis Generation?

Of course, hypothesis generation is not always straightforward. Understanding the earlier examples is easy for us because we're already inundated with context. But, in a situation where an analyst has no domain knowledge, suddenly, hypothesis generation becomes a tedious and challenging process.

AI, particularly high-capacity, robust tools such as LLMs, have radically changed how we process and analyse large volumes of data. With its help, we can sift through massive datasets with precision and speed, regardless of context, whether it's customer behaviour, financial trends, medical records, or more. Generative AI, including LLMs, are trained on diverse text data, enabling them to comprehend and process various topics.

Now, imagine an AI assistant helping you with hypothesis generation. LLMs are not born with context. Instead, they are trained upon vast amounts of data, enabling them to develop context in a completely unfamiliar environment. This skill is instrumental when adopting a more exploratory approach to hypothesis generation. For example, the HR leader from earlier could simply ask an LLM tool: “Can you look at this employee productivity data and find cohorts of high-productivity and see if they correlate to any other employee data like location, pedigree, years of service, marital status, etc?” 

For an LLM-based tool to be useful, it requires a few things:

• Domain Knowledge: A human could take months to years to acclimatise to a particular field fully, but LLMs, when fed extensive information and utilising Natural Language Processing (NLP), can familiarise themselves in a very short time.
• Explainability:   Explainability is its ability to explain its thought process and output to cease being a "black box".
• Customisation: For consistent improvement, contextual AI must allow tweaks, allowing users to change its behaviour to meet their expectations. Human intervention and validation is a necessary step in adoptingAI tools. NLP allows these tools to discern context within textual data, meaning it can read, categorise, and analyse data with unimaginable speed. LLMs, thus, can quickly develop contextual understanding and generate human-like text while processing vast amounts of unstructured data, making it easier for businesses and researchers to organise and utilise data effectively.LLMs have the potential to become indispensable tools for businesses. The future rests on AI tools that harness the powers of LLMs and NLP to deliver actionable insights, mitigate risks, inform decision-making, predict future trends, and drive business transformation across various sectors.

Together, these technologies empower data analysts to unravel hidden insights within their data. For our pen maker, for example, an AI tool could aid data analytics. It can look through historical data to track when sales peaked or go through sales data to identify the pens that sold the most. It can refine a hypothesis across iterations, just as a human analyst would. It can even be used to brainstorm other hypotheses. Consider the situation where you ask the LLM, " Where do I sell the most pens? ". It will go through all of the data you have made available - places where you sell pens, the number of pens you sold - to return the answer. Now, if we were to do this on our own, even if we were particularly meticulous about keeping records, it would take us at least five to ten minutes, that too, IF we know how to query a database and extract the needed information. If we don't, there's the added effort required to find and train such a person. An AI assistant, on the other hand, could share the answer with us in mere seconds. Its finely-honed talents in sorting through data, identifying patterns, refining hypotheses iteratively, and generating data-backed insights enhance problem-solving and decision-making, supercharging our business model.

Top-Down and Bottom-Up Hypothesis Generation

As we discussed earlier, every hypothesis begins with a default action that determines your initial hypotheses and all your subsequent data collection. You look at data and a LOT of data. The significance of your data is dependent on the effect and the relevance it has to your default action. This would be a top-down approach to hypothesis generation.

There is also the bottom-up method , where you start by going through your data and figuring out if there are any interesting correlations that you could leverage better. This method is usually not as focused as the earlier approach and, as a result, involves even more data collection, processing, and analysis. AI is a stellar tool for Exploratory Data Analysis (EDA). Wading through swathes of data to highlight trends, patterns, gaps, opportunities, errors, and concerns is hardly a challenge for an AI tool equipped with NLP and powered by LLMs.

EDA can help with: 

• Cleaning your data
• Understanding your variables
• Analysing relationships between variables

An AI assistant performing EDA can help you review your data, remove redundant data points, identify errors, note relationships, and more. All of this ensures ease, efficiency, and, best of all, speed for your data analysts.

Good hypotheses are extremely difficult to generate. They are nuanced and, without necessary context, almost impossible to ascertain in a top-down approach. On the other hand, an AI tool adopting an exploratory approach is swift, easily running through available data - internal and external. 

If you want to rearrange how your LLM looks at your data, you can also do that. Changing the weight you assign to the various events and categories in your data is a simple process. That’s why LLMs are a great tool in hypothesis generation - analysts can tailor them to their specific use cases. 

Ethical Considerations and Challenges

There are numerous reasons why you should adopt AI tools into your hypothesis generation process. But why are they still not as popular as they should be?

Some worry that AI tools can inadvertently pick up human biases through the data it is fed. Others fear AI and raise privacy and trust concerns. Data quality and ability are also often questioned. Since LLMs and Generative AI are developing technologies, such issues are bound to be, but these are all obstacles researchers are earnestly tackling.

One oft-raised complaint against LLM tools (like OpenAI's ChatGPT) is that they 'fill in' gaps in knowledge, providing information where there is none, thus giving inaccurate, embellished, or outright wrong answers; this tendency to "hallucinate" was a major cause for concern. But, to combat this phenomenon, newer AI tools have started providing citations with the insights they offer so that their answers become verifiable. Human validation is an essential step in interpreting AI-generated hypotheses and queries in general. This is why we need a collaboration between the intelligent and artificially intelligent mind to ensure optimised performance.

Clearly, hypothesis generation is an immensely time-consuming activity. But AI can take care of all these steps for you. From helping you figure out your default action, determining all the major research questions, initial hypotheses and alternative actions, and exhaustively weeding through your data to collect all relevant points, AI can help make your analysts' jobs easier. It can take any approach - prospective, retrospective, exploratory, top-down, bottom-up, etc. Furthermore, with LLMs, your structured and unstructured data are taken care of, meaning no more worries about messy data! With the wonders of human intuition and the ease and reliability of Generative AI and Large Language Models, you can speed up and refine your process of hypothesis generation based on feedback and new data to provide the best assistance to your business.

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HypothesisHub is an AI Tool for the Automated Generation of Research Questions and Hypotheses from Scientific Literature. It applies a chain of reasoning to scientific literature to generate questions and hypotheses. OpenAI and Langchain serve as the underlying technologies for the tool.

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• Generates a null hypothesis (H0) and an alternate hypothesis (H1) for each research question
• Handles cases where either H0 or H1 is not present
• Automatically generates missing H1 using the LLMChain if needed
• Negates hypothesis statement if H0 is missing

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Online Hypothesis Generator

Forge precise, research-backed hypotheses in a snap using our top-notch hypothesis creator, ensuring your study starts on solid ground..

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• 1. Start by by indicating the positive or negative trajectory of your hypothesis in the "Effect" section.
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Key Steps on Writing Proper Research Hypothesis with EssayGPT

Tapping into the potential of EssayGPT's hypothesis generator can revolutionize your research process. However, to optimize the AI's capabilities, a few key considerations can significantly enhance the coherence and relevance of the generated hypotheses.

Here's a deeper dive into those nuances.

Precision in Input: The tool's prowess lies in its ability to interpret and process the information it's given. Just as a finely tuned instrument delivers the best music, clear and specific inputs allow the generator to produce accurate hypotheses. Being vague or too broad might lead to generic outcomes that don’t precisely serve your research aims.

Alignment with Research Context: The essence of a valuable hypothesis is its seamless fit within the broader research landscape. It's not just about a statement, but one that directly speaks to, and illuminates, the research problem or question you're addressing. By ensuring that the generated hypothesis aligns contextually, you guarantee its relevance and applicability.

Vocabulary Matters: Each field of study has its lexicon. Incorporating field-specific terms or jargon can transform a generic statement into a specialized hypothesis. It’s not just about linguistic accuracy, but about imbuing your hypothesis with the depth and resonance pertinent to your study's discipline.

The Human Element: AI is a powerful tool, but it's the human touch that brings depth, intuition, and context. After the AI crafts the hypothesis, it's beneficial to weave in personal insights or adjust nuances. This ensures that while the hypothesis is technically sound, it also captures the unique intricacies and flavors of individual research endeavors.

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Absolutely. The hypothesis creator harnesses a state-of-the-art AI algorithm, expertly designed to navigate even the most complex and multidimensional subjects. Leveraging advanced contextual comprehension coupled with vast datasets, the tool is adept at crafting accurate hypotheses irrespective of topic intricacy.

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• Open access
• Published: 29 August 2012

An automated framework for hypotheses generation using literature

• Vida Abedi 1 , 2 ,
• Ramin Zand 3 ,
• Mohammed Yeasin 1 , 2 &
• Fazle Elahi Faisal 1 , 2  

BioData Mining volume  5 , Article number:  13 ( 2012 ) Cite this article

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In bio-medicine, exploratory studies and hypothesis generation often begin with researching existing literature to identify a set of factors and their association with diseases, phenotypes, or biological processes. Many scientists are overwhelmed by the sheer volume of literature on a disease when they plan to generate a new hypothesis or study a biological phenomenon. The situation is even worse for junior investigators who often find it difficult to formulate new hypotheses or, more importantly, corroborate if their hypothesis is consistent with existing literature. It is a daunting task to be abreast with so much being published and also remember all combinations of direct and indirect associations. Fortunately there is a growing trend of using literature mining and knowledge discovery tools in biomedical research. However, there is still a large gap between the huge amount of effort and resources invested in disease research and the little effort in harvesting the published knowledge. The proposed hypothesis generation framework (HGF) finds “crisp semantic associations” among entities of interest - that is a step towards bridging such gaps.

Methodology

The proposed HGF shares similar end goals like the SWAN but are more holistic in nature and was designed and implemented using scalable and efficient computational models of disease-disease interaction. The integration of mapping ontologies with latent semantic analysis is critical in capturing domain specific direct and indirect “crisp” associations, and making assertions about entities (such as disease X is associated with a set of factors Z).

Pilot studies were performed using two diseases. A comparative analysis of the computed “associations” and “assertions” with curated expert knowledge was performed to validate the results. It was observed that the HGF is able to capture “crisp” direct and indirect associations, and provide knowledge discovery on demand.

Conclusions

The proposed framework is fast, efficient, and robust in generating new hypotheses to identify factors associated with a disease. A full integrated Web service application is being developed for wide dissemination of the HGF. A large-scale study by the domain experts and associated researchers is underway to validate the associations and assertions computed by the HGF.

Peer Review reports

The explosion of OMICS - based technologies, such as genomics, proteomics, and pharmaco-genomics, has generated a wave of information retrieval tools, such as SWAN [ 1 ], to mine the heterogeneous, high dimensional and large databases, as well as complex biological networks. The general characteristics of such complex systems as well as their robustness and dynamical properties were reported by many researchers (i.e., [ 2 , 3 ]). These reports of designing scalable and efficient knowledge discovery tools can further our understanding of complex biological systems. The burgeoning gap between the effort and investment made to acquire the knowledge about complexities of biological systems is disproportionately large compared to the development of knowledge discovery tools that can be used for effectively disseminating the acquired knowledge, generating and validating hypothesis, and understanding the complex causal relationships. Despite a plethora of efforts in reverse-engineering of complex systems to predict response to perturbations, there is a lack of significant effort to create a higher level abstraction of such complex biological systems using sources of information other than genetics data [ 2 , 4 ]. A high level view of complex systems would be very useful in generating new hypotheses and connecting seemingly unrelated entities. Such an abstraction could facilitate translational research and may prove vital in clinical studies by providing a valuable reference to the clinicians, researchers, and other domain experts.

Disease networks can provide a high level view of complex systems; however, the reported networks are mostly based on genetic and proteomic data [ 2 , 4 ]. Such networks could also be constructed based on literature data to incorporate a wider range of factors such as side effects and risk factors. Generating disease-models based on literature data is a very natural and efficient way to better understand and summarize the current knowledge about different high-level systems. A connection between two diseases can be formalized by risk factors, symptoms, treatment options, or any other diseases as compared to only common disease-genes. The relations between diseases can provide a systematic approach to identify missing links and potential associations. It will also create new avenues for collaborations and interdisciplinary research.

To construct a disease network based on literature data, it is imperative to have a scalable and efficient literature-mining tool to explore the huge textual resources. Nevertheless, mining of biological and medical literature is a very challenging task [ 5 – 7 ]. This can further be complicated by challenges with the implementation of relevant information extraction, also known as deep parsing, which is built on formal mathematical models. Deep parsing, also known as formal grammar, attempts to describe how text is generated in the human mind [ 5 ]. Deterministic or probabilistic context-free grammars are probably the most popular formal grammars [ 7 ]. Grammar-based information extraction techniques are computationally expensive as they require the evaluation of alternative ways to generate the same sentence. Grammar-based information could therefore be more precise but at the cost of reduced processing speed [ 5 ].

An alternative to the grammar-based methods are factorization methods such as Latent Semantic Analysis (LSA) [ 8 ], and Non-negative Matrix Factorization (NMF) [ 9 , 10 ]. Factorization methods rely on bag-of-word concept, and have therefore reduced computational complexity. LSA is a well known information retrieval technique which has been applied to many areas in bioinformatics. Arguably, LSA captures semantic relations between various concepts based on their distance in the reduced eigen space [ 11 ]. It has the advantage of extracting direct and indirect associations between entities. A commonly used distance measure in LSA is the cosine value of the angle between the document and query in the reduced eigen space.

Over the past two decades, medical text-mining has proved to be valuable in generating new exciting hypotheses. For instance, titles from MEDLINE were used to make connections between disconnected arguments: 1) the connection between migraine and magnesium deficiency [ 12 ] which has been verified experimentally; 2) between indomethacin and Alzheimer’s disease [ 12 ]; and finally 3) between Curcuma longa and retinal diseases [ 13 ]. Hypothesis generation in literature-mining relies on the fact that chance connections can emerge to be meaningful [ 7 ].

This paper designs and implements an efficient and scalable literature-mining framework to generate and also validate plausible hypotheses about various entities that include (but not limited to): risk factors, environmental factors, lifestyle, diseases, and disease groups. The proposed hypothesis generation framework (HGF) is implemented based on parameter optimized latent semantic analysis (POLSA) [ 14 ] and is suitable to capture direct and indirect association among concepts. It is easy to note that the overall performance and quality of results obtained through LSA-based systems is a function of the dictionary used. The concept of mapping ontologies was integrated with the POLSA to overcome such limitations and to provide crisp associations. In particular, the Medical Subject Headings (MESH) is used to construct the dictionary. Such a dictionary allows a more efficient use of the LSA technique in finding semantically related entities in the biological and medical sciences. This framework can be used to generate customized disease-disease interaction networks, to facilitate interdisciplinary collaborations between scientists and organizations, to discover hidden knowledge, and to spawn new research directions. In addition, the concept of statistical disease modeling was introduced to compute the strongly related, related, and not related concepts.

The following section describes the proposed hypothesis generation framework and its evaluation. Two case studies were performed to showcase the potential and utility of the proposed method. Finally, the paper ends with a brief conclusion and discussions about the strengths and weaknesses of the method.

Results and discussion

Hypotheses generation framework (hgf).

The HGF has three major modules: Ontology Mapping to generate data-driven domain specific dictionaries, a parameter optimized latent semantic analysis (POLSA), and Disease Model. The schematic diagram of the overall HGF framework is shown in the Figure 1 (A). The model is constructed using the POLSA framework, and it is based on the selected documents and the dictionary (Figure 1 C). Users can query the model and the output is a ranked list of headings. These ranked headings are grouped into three sets (unknown factors, potential factors, or established factors) using the Disease Model module (Figure 1 C and 1 D). Analyzing the headings in the three sets can facilitate hypothesis generation and information retrieval based on user query.

Flow diagram of the hypothesis generation framework (HGF). A ) In a medical and biological setting, Ontology Mapping could use the Medical Subject Heading (MeSH) and generate a context specific dictionary, which is one of the parameters of the POLSA model. Associated factors are ranked based on a User Query which can be any word(s) in the dictionary. These factors are subsequently grouped into three different bins (unknown factors, potential factors or established factors) based on our Disease Model. B ) Ontology Mapping to create domain specific dictionary. C ) Parameter Optimized Latent Semantic Analysis Module. D ) Disease Model Module.

Ontology mapping

MeSH is used to generate the dictionary in the POLSA model. The mapping of MeSH ontology to create the dictionary for the POLSA significantly enhances the quality of results and provides a crisp association of semantically related entities in biological and medical science. All MeSH headings are reduced to single words to create the context specific and data driven dictionary (see Figure 1 B). For instance, “Reproductive and Urinary Physiological Phenomena” is a MeSH term and is reduced to five words in the dictionary (1. Reproductive, 2. and, 3. Urinary, 4. Physiological, and 5. Phenomena). In the filtering step, duplicates as well as stop words such as “and” or words containing fewer than three characters are removed. The final size of this dictionary is 19,165 words. Any dictionary word could be used as a query to the HGF. For instance, the disease “stroke” is a query in this study. The highly ranked factors with respect to a query-disease are considered factors associated with that disease. Cosine similarity measure is used as a metric in the HGF.

POLSA module

In order to develop an effective literature-mining framework to model disease-disease interaction networks, generate plausible new hypotheses, and support knowledge-discovery by finding semantically related entities, a Parameter Optimized LSA (POLSA) [ 14 ] was re-designed and adopted in the proposed HGF framework.

In addition, a set of associated factors was selected to represent interaction between diseases. Ninety-six common associated factors (see Table 1 ) were selected through a literature review from numerous medical articles by two domain experts. As the first step, a set of articles was selected by querying the PubMed database using a series of diseases and factors. In the second step, the retrieved articles were manually reviewed by domain experts and entities that were associated with diseases or factors were selected. All articles considered for this analysis were peer reviewed articles. In addition, some common diseases such as diabetes and depression were also included in the set of 96 factors, as these are believed to be, in many instances, risk factors to other diseases. Therefore, the set of 96 associated factors represents a wide range of factors including generic factors such as depression and infection as well as specific factors such as vitamin E. As the final step, the set was further revised by an expert in the medical field. Using the improved POLSA technique [ 14 ], meaningful associations from the textual data in the PubMed database are extracted and mined. Furthermore, the factors are ranked based on their level of association to a given query.

Titles and abstracts from PubMed (for the past twenty years) for each of the 96 factors were downloaded in a local machine. On average there were 47,570 abstracts per factor; the specific factors such as “maternal influenza” had fewer abstracts associated with them (minimum of 160 abstracts/factor) and the more generic factors such as “hormone” were associated with a greater number of abstracts (a maximum of 557,554 abstracts/factor). The complete collection was then used to construct the knowledge space for the POLSA model. Using a query such as “Parkinson” or “stroke” the 96 factors were then ranked based on their relative level of associations to the query. The distribution of a set of associated factors with respect to a disease was modeled as a tri-modal distribution: a distribution which has three modes. This is due to the fact that some factors are known to be associated with the disease and have high scores. Similarly, some factors are known to be unassociated to the disease and these have negative scores; in addition, some factors may or may not be associated to the disease and these have low similarity scores. Matlab was used to generate two tri-modal distributions based on general Gaussian models for the two distributions obtained from queries “stroke” and “Parkinson”. The model uses the following formulation to describe the tri-modal Gaussian distribution:

Where α 1 , α 2 and α 3 are the scaling factors; μ 1 , μ 2 and μ 3 are the position of the center of the peaks, and σ 1 , σ 2 , σ 3 control the width of the distributions. The goodness of fit was measured using an R-square score.

• Disease model

Using a disease model (see Figure 2 ), it was possible to map the mixture of three Gaussian distributions into easy to understandable categories. The implicit assumption is that if associated factors of a disease are well known, a large body of literature will be available to corroborate the existence of such associations. On the other hand, if associated factors of a disease are not well documented, the factors are weakly associated to the disease with few factors displaying a high level of association (Disease X versus Disease Y as shown in the Figure 2 ). Since the distribution of association level of factors (including risk factors) will be different in the two scenarios. In the first case (Disease Y) the two dominating distributions are the factors that are associated and those that are not associated with the disease; in the second case (Disease X) the dominating distribution is that of the potential factors. In essence, if one accepts this assumption then the distribution of associated factors follows a tri-modal distribution and it will be intuitive to measure the level of association for different factors with respect to a given disease. Utilization of a disease model (by a tri-modal distribution) allows better identification of the three sets of factors: unknown associations, potential associations and established associations.

Model for the distribution of associated factors of a given disease. If associated factors – such as risk factors – of a disease are well known as in the case for Disease Y, then the two dominating distributions are the factors that are associated and those that are not associated with the disease; if on other hand the associated factors of a disease are not well documented (Disease X) then the dominating distribution is that of the potential factors.

Separating the three distributions allows implementation of a dynamic and data-driven threshold calculation. Hence, the parameters of the distributions can be used to model a cut-off threshold for the factors that are established, potential, or unknown. This method is empirical and provides an intuitive approach to evaluate the results. The score can be further optimized in a heuristic manner with utilization of a large-scale and comprehensive ground truth set. Furthermore, the highly associated factors to the disease are the well known factors; the hidden knowledge on the other hand resides in the region where the associations are positive yet weak.

Model evaluation

Two diseases, namely, Ischemic Stroke (IS) and Parkinson’s Disease (PD), were used as queries to the hypothesis generation system. The distribution of associated factors is presented in the Figure 3 . The results were compared with MedLink neurology [ 15 ], a web resource used by clinicians. Comparative results were summarized in the Figure 4 . In the case of IS, most of the associated factors are identified by both systems; however there is a set of factors that have only been identified by the proposed approach. In the case of the PD, a large number of factors have been identified by both systems. However, there are a number of factors that have only been identified by the proposed HGF and only a handful that are mentioned in the MedLink neurology which have positive but low similarity score in the hypothesis generation framework.

Number of factors identified by MedLink Neurology and by HGF for IS and PD. Association levels for IS measured by HGF are high (0.3 < cosine score) and possible (0.1 < cosine score < 0.3); association levels for PD measured by HGF are high (0.2 < cosine score), possible (0.1 < cosine score < 0.2) or low (0.05 < cosine score < 0.1).

Distribution of similarity score (dashed line) for risk factors associated with IS and PD. The frequency represents the number of factors at each cosine similarity level (−1 to +1). Tri-modal distribution models are represented by solid lines.

The tri-modal distribution model is used to group the associated factors into different levels. The cut-off values to differentiate between different association levels vary slightly depending on the distribution of the similarity scores. The ideal decision boundary can be found if a large number of ground truth cases are available; in this situation the decision boundary is selected intuitively based on the shape of the distributions. For example, in the case of IS, factors are considered highly associated if their cosine score is greater than 0.3, factors are possible associated if their score is between 0.1 and 0.3 and are possibly not associated if their score is lower than 0.1. In the case of PD, factors are considered highly associated if their cosine score is greater than 0.2, factors are possibly associated if their cosine score is between 0.1 and 0.2 and finally the factors with scores between 0.05 and 0.1 are considered associated at low level, factors with scores lower than 0.05 are considered possibly not associated with the Parkinson’s Disease.

In the case of IS, the distribution of known associated factors are more shifted to the right as compared to the factors in PD, hence the separation between the known and unknown factors is more pronounced. In addition to that, associations at both extreme levels (close to +1.0 and −1.0) are likely to be common knowledge; however, the hidden knowledge tends to be captured at similarity scores that are low yet positive. Nonetheless, it is not realistic to compare the precise similarity score values in order to give more importance to one factor versus another factor mainly because there is a systemic bias that is inherent to the biological text data and causes the generic factors to be an underestimate of the true value (data not shown); hence a direct comparison would fail in this case if no additional normalization steps are taken.

Figure 3 summarizes a comparative analysis of MedLink Neurology and HGF for IS and PD. Overall in the case of IS, twelve factors were identified by both systems and six factors were identified by the HGF. In the case of PD, twelve factors were identified by both systems, ten factors were identified by the HGF and five factors were identified by MedLink Neurology. But, these factors had a low association level in HGF. The five factors were either very generic or were not exactly mapped in the set of the 96 factors, hence a direct comparison could not be made. Finally, this small scale comparative analysis corroborates the hypothesis that HGF based on literature can better predict the associated factors for diseases such as IS when the risk and associated factors are well studied and documented. In both cases, MedLink, Neurology, and HGF predicted twelve common associated factors; however, in the case of PD ten new factors were predicted in comparison to six in the case of IS.

De novo hypothesis generation can provide an approach on how we design experiments and select the parameters for the study. Interestingly, associations detected by the proposed framework can facilitate extraction of interesting observations and new trends in the field. For instance, it was found that PD could possibly be associated with immunological disorders; this is an intriguing observation. This analysis also facilitates interdisciplinary research and enhances interaction among scientists from sub-specialized fields. A manual review of the literature is performed to find evidences for some of the associations found only by the HGF; Table 2 summarizes these results.

There are three main limitations in the presented framework. We are currently in the process of finding solutions for these limitations. 1) Manual selection of the factors creates bias in the dataset and also limits its scalability property. To alleviate this problem, MeSH hierarchy will be used to generate the set of factors. MeSH comprises more than 25,000 subjects headings organized in an eleven-level hierarchy. 2) In the set of 96 factors, some factors were very generic and some very specific, therefore, there was a systemic bias in the dataset which caused the score for generic factors to be an underestimate of the true values and factors with limited information to be overestimated (data not shown). To partially solve this technical difficulty, an improved method based on local LSA is being developed in our lab. And finally, 3) looking only at literature from the past twenty years was not sufficient for the HGF. The expansion of the literature is necessary based on the observation that the association between head trauma and PD was significantly lower than expected.

Generating new hypotheses by mining a vast amount of raw unstructured knowledge from the archived reported literature may help in identifying new research trends as well as promoting interdisciplinary studies. In addition, the presented framework is not limited to uncovering disease-disease interactions; any word from MeSH can be used to query the system, and its associated factors can be identified accordingly. Disease-disease interaction networks, interaction networks among chemical compounds, drug-drug interaction networks, or any specific type of interaction network can be constructed using the HGF. The common basis for all these networks is the knowledge embedded in the literature. Application of this framework is broad as its usage is not limited to any specific domain. For instance, uncovering drug-drug interactions is valuable in drug development and drug administration, uncovering disease-disease interaction is important in understanding disease mechanism’s and advancing biology through integrated interdisciplinary research. Even though the framework is not limited to diseases, in this study two neurological diseases were used to test the system and demonstrate the power and applicability of the framework.

In addition to addressing the limitations of the framework, work is in progress to expand the HGF framework to allow the user to generate disease networks based on a number of user-defined queries. Such customized networks can be valuable to a wide range of scientists by promoting a faster identification of associated factors and detection of disease-disease interactions. Disease networks based on genetics and proteomics data display many connections between individual disorders and disease categories [ 2 , 4 ]. Therefore, as expected each human disorder does not seem to have unique origins or be independent of other disorders. To uncover potential links between two disorders knowledge extraction from medical literature could be greatly beneficial and reliable.

Authors’ information

VA is a Ph.D. candidate in Electrical and Computer Engineering at the University of Memphis; she has a B.A.Sc. in Computer Engineering and B.Sc. in Biochemistry in addition to a M.Sc. in Cellular Molecular Medicine and a second M.Sc. in Bioinformatics. Her research interests are interdisciplinary research in Medical Informatics and Systems Biology. VA’s research incorporates a systems approach to understanding gene regulatory networks, which combines mathematical modeling and molecular biology wet lab techniques. Her recent contributions are in medical informatics where her board understanding of interdisciplinary issues as well as deep knowledge in mathematics and experimental biology are fundamental in designing and performing experiments in translational research.

RZ is a M.D. in the department of Neurology at the University of Tennessee. He also holds a Masters of Public Health. His research interests include Vascular Neurology and Bioinformatics. Over the past few years, RZ has contributed to bridge the gap between clinical findings and application of bioinformatics tools.

FEF is a PhD candidate in Electrical and Computer Engineering at The University of Memphis; he has a B.Sc. in Computer Science and Engineering, M.Sc. degree in Computer Science and Engineering and a second M.Sc. degree in Bioinformatics. His research interests are biological information retrieval and data mining. FEF possesses good knowledge in software design and development. He participated in software development of some national and international research projects, such as Codewitz Asia-Link Project of European Union.

MY is an Associate Professor in the department of Electrical and Computer Engineering, adjunct faculty member of Biomedical Engineering and Bioinformatics Program, and an affiliated member of the Institute for Intelligent Systems (IIS) at The University of Memphis (U of M). He is a senior member of the IEEE. He made significant contributions in the research and development of real-time computer vision solutions for academic research and commercial applications. He has been involved with several technological innovations, including classifying gender, age group, ethnicity and emotion, face detection, recognition of human activities in video, and speech-gesture enabled sophisticated natural human-computer interfaces. Some of his research on facial image analysis and hand gesture recognition is used in developing several commercial products by the Videomining Inc.

Abbreviations

Hypothesis generation framework

Ischemic stroke

Latent semantic analysis

Medical subject heading

Non-negative matrix factorization

Parkinson’s disease

Parameter optimized latent semantic analysis.

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Acknowledgements

This work was supported by the Electrical and Computer Engineering Department and Bioinformatics Program at the University of Memphis, by the University of Tennessee Health Science Center (UTHSC), as well as by NSF grant NSF-IIS-0746790. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding institution.

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VA designed and carried out the experiments, participated in the development of the methods, analyzed the results and drafted the manuscript. RZ participated in the development of the methods, designed the validation experiments for the two test cases and reviewed the manuscript. FEF participated in the implementation of the algorithms. MY participated in the development of the methods, supervised the experiments and edited the manuscript. All authors have read, and approved the final version of the manuscript.

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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Lesson 10 of 24 By Avijeet Biswal

Table of Contents

In today’s data-driven world, decisions are based on data all the time. Hypothesis plays a crucial role in that process, whether it may be making business decisions, in the health sector, academia, or in quality improvement. Without hypothesis and hypothesis tests, you risk drawing the wrong conclusions and making bad decisions. In this tutorial, you will look at Hypothesis Testing in Statistics.

What Is Hypothesis Testing in Statistics?

Hypothesis Testing is a type of statistical analysis in which you put your assumptions about a population parameter to the test. It is used to estimate the relationship between 2 statistical variables.

Let's discuss few examples of statistical hypothesis from real-life - 

• A teacher assumes that 60% of his college's students come from lower-middle-class families.
• A doctor believes that 3D (Diet, Dose, and Discipline) is 90% effective for diabetic patients.

Now that you know about hypothesis testing, look at the two types of hypothesis testing in statistics.

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Importance of Hypothesis Testing in Data Analysis

Here is what makes hypothesis testing so important in data analysis and why it is key to making better decisions:

Avoiding Misleading Conclusions (Type I and Type II Errors)

One of the biggest benefits of hypothesis testing is that it helps you avoid jumping to the wrong conclusions. For instance, a Type I error could occur if a company launches a new product thinking it will be a hit, only to find out later that the data misled them. A Type II error might happen when a company overlooks a potentially successful product because their testing wasn’t thorough enough. By setting up the right significance level and carefully calculating the p-value, hypothesis testing minimizes the chances of these errors, leading to more accurate results.

Making Smarter Choices

Hypothesis testing is key to making smarter, evidence-based decisions. Let’s say a city planner wants to determine if building a new park will increase community engagement. By testing the hypothesis using data from similar projects, they can make an informed choice. Similarly, a teacher might use hypothesis testing to see if a new teaching method actually improves student performance. It’s about taking the guesswork out of decisions and relying on solid evidence instead.

Optimizing Business Tactics

In business, hypothesis testing is invaluable for testing new ideas and strategies before fully committing to them. For example, an e-commerce company might want to test whether offering free shipping increases sales. By using hypothesis testing, they can compare sales data from customers who received free shipping offers and those who didn’t. This allows them to base their business decisions on data, not hunches, reducing the risk of costly mistakes.

Hypothesis Testing Formula

Z = ( x̅ – μ0 ) / (σ /√n)

• Here, x̅ is the sample mean,
• μ0 is the population mean,
• σ is the standard deviation,
• n is the sample size.

How Hypothesis Testing Works?

An analyst performs hypothesis testing on a statistical sample to present evidence of the plausibility of the null hypothesis. Measurements and analyses are conducted on a random sample of the population to test a theory. Analysts use a random population sample to test two hypotheses: the null and alternative hypotheses.

The null hypothesis is typically an equality hypothesis between population parameters; for example, a null hypothesis may claim that the population means return equals zero. The alternate hypothesis is essentially the inverse of the null hypothesis (e.g., the population means the return is not equal to zero). As a result, they are mutually exclusive, and only one can be correct. One of the two possibilities, however, will always be correct.

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Null Hypothesis and Alternative Hypothesis

The Null Hypothesis is the assumption that the event will not occur. A null hypothesis has no bearing on the study's outcome unless it is rejected.

H0 is the symbol for it, and it is pronounced H-naught.

The Alternate Hypothesis is the logical opposite of the null hypothesis. The acceptance of the alternative hypothesis follows the rejection of the null hypothesis. H1 is the symbol for it.

Let's understand this with an example.

A sanitizer manufacturer claims that its product kills 95 percent of germs on average. 

To put this company's claim to the test, create a null and alternate hypothesis.

H0 (Null Hypothesis): Average = 95%.

Alternative Hypothesis (H1): The average is less than 95%.

Another straightforward example to understand this concept is determining whether or not a coin is fair and balanced. The null hypothesis states that the probability of a show of heads is equal to the likelihood of a show of tails. In contrast, the alternate theory states that the probability of a show of heads and tails would be very different.

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Hypothesis Testing Calculation With Examples

Let's consider a hypothesis test for the average height of women in the United States. Suppose our null hypothesis is that the average height is 5'4". We gather a sample of 100 women and determine their average height is 5'5". The standard deviation of population is 2.

To calculate the z-score, we would use the following formula:

z = ( x̅ – μ0 ) / (σ /√n)

z = (5'5" - 5'4") / (2" / √100)

z = 0.5 / (0.045)

We will reject the null hypothesis as the z-score of 11.11 is very large and conclude that there is evidence to suggest that the average height of women in the US is greater than 5'4".

Steps in Hypothesis Testing

Hypothesis testing is a statistical method to determine if there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. Here’s a breakdown of the typical steps involved in hypothesis testing:

Formulate Hypotheses

• Null Hypothesis (H0): This hypothesis states that there is no effect or difference, and it is the hypothesis you attempt to reject with your test.
• Alternative Hypothesis (H1 or Ha): This hypothesis is what you might believe to be true or hope to prove true. It is usually considered the opposite of the null hypothesis.

Choose the Significance Level (α)

The significance level, often denoted by alpha (α), is the probability of rejecting the null hypothesis when it is true. Common choices for α are 0.05 (5%), 0.01 (1%), and 0.10 (10%).

Select the Appropriate Test

Choose a statistical test based on the type of data and the hypothesis. Common tests include t-tests, chi-square tests, ANOVA, and regression analysis. The selection depends on data type, distribution, sample size, and whether the hypothesis is one-tailed or two-tailed.

Collect Data

Gather the data that will be analyzed in the test. To infer conclusions accurately, this data should be representative of the population.

Calculate the Test Statistic

Based on the collected data and the chosen test, calculate a test statistic that reflects how much the observed data deviates from the null hypothesis.

Determine the p-value

The p-value is the probability of observing test results at least as extreme as the results observed, assuming the null hypothesis is correct. It helps determine the strength of the evidence against the null hypothesis.

Make a Decision

Compare the p-value to the chosen significance level:

• If the p-value ≤ α: Reject the null hypothesis, suggesting sufficient evidence in the data supports the alternative hypothesis.
• If the p-value > α: Do not reject the null hypothesis, suggesting insufficient evidence to support the alternative hypothesis.

Report the Results

Present the findings from the hypothesis test, including the test statistic, p-value, and the conclusion about the hypotheses.

Perform Post-hoc Analysis (if necessary)

Depending on the results and the study design, further analysis may be needed to explore the data more deeply or to address multiple comparisons if several hypotheses were tested simultaneously.

Types of Hypothesis Testing

To determine whether a discovery or relationship is statistically significant, hypothesis testing uses a z-test. It usually checks to see if two means are the same (the null hypothesis). Only when the population standard deviation is known and the sample size is 30 data points or more, can a z-test be applied.

A statistical test called a t-test is employed to compare the means of two groups. To determine whether two groups differ or if a procedure or treatment affects the population of interest, it is frequently used in hypothesis testing.

3. Chi-Square 

You utilize a Chi-square test for hypothesis testing concerning whether your data is as predicted. To determine if the expected and observed results are well-fitted, the Chi-square test analyzes the differences between categorical variables from a random sample. The test's fundamental premise is that the observed values in your data should be compared to the predicted values that would be present if the null hypothesis were true.

ANOVA , or Analysis of Variance, is a statistical method used to compare the means of three or more groups. It’s particularly useful when you want to see if there are significant differences between multiple groups. For instance, in business, a company might use ANOVA to analyze whether three different stores are performing differently in terms of sales. It’s also widely used in fields like medical research and social sciences, where comparing group differences can provide valuable insights.

Hypothesis Testing and Confidence Intervals

Both confidence intervals and hypothesis tests are inferential techniques that depend on approximating the sample distribution. Data from a sample is used to estimate a population parameter using confidence intervals. Data from a sample is used in hypothesis testing to examine a given hypothesis. We must have a postulated parameter to conduct hypothesis testing.

Bootstrap distributions and randomization distributions are created using comparable simulation techniques. The observed sample statistic is the focal point of a bootstrap distribution, whereas the null hypothesis value is the focal point of a randomization distribution.

A variety of feasible population parameter estimates are included in confidence ranges. In this lesson, we created just two-tailed confidence intervals. There is a direct connection between these two-tail confidence intervals and these two-tail hypothesis tests. The results of a two-tailed hypothesis test and two-tailed confidence intervals typically provide the same results. In other words, a hypothesis test at the 0.05 level will virtually always fail to reject the null hypothesis if the 95% confidence interval contains the predicted value. A hypothesis test at the 0.05 level will nearly certainly reject the null hypothesis if the 95% confidence interval does not include the hypothesized parameter.

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Simple and Composite Hypothesis Testing

Depending on the population distribution, you can classify the statistical hypothesis into two types.

Simple Hypothesis: A simple hypothesis specifies an exact value for the parameter.

Composite Hypothesis: A composite hypothesis specifies a range of values.

A company is claiming that their average sales for this quarter are 1000 units. This is an example of a simple hypothesis.

Suppose the company claims that the sales are in the range of 900 to 1000 units. Then this is a case of a composite hypothesis.

One-Tailed and Two-Tailed Hypothesis Testing

The One-Tailed test, also called a directional test, considers a critical region of data that would result in the null hypothesis being rejected if the test sample falls into it, inevitably meaning the acceptance of the alternate hypothesis.

In a one-tailed test, the critical distribution area is one-sided, meaning the test sample is either greater or lesser than a specific value.

In two tails, the test sample is checked to be greater or less than a range of values in a Two-Tailed test, implying that the critical distribution area is two-sided.

If the sample falls within this range, the alternate hypothesis will be accepted, and the null hypothesis will be rejected.

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Right Tailed Hypothesis Testing

If the larger than (>) sign appears in your hypothesis statement, you are using a right-tailed test, also known as an upper test. Or, to put it another way, the disparity is to the right. For instance, you can contrast the battery life before and after a change in production. Your hypothesis statements can be the following if you want to know if the battery life is longer than the original (let's say 90 hours):

• The null hypothesis is (H0 <= 90) or less change.
• A possibility is that battery life has risen (H1) > 90.

The crucial point in this situation is that the alternate hypothesis (H1), not the null hypothesis, decides whether you get a right-tailed test.

Left Tailed Hypothesis Testing

Alternative hypotheses that assert the true value of a parameter is lower than the null hypothesis are tested with a left-tailed test; they are indicated by the asterisk "<".

Suppose H0: mean = 50 and H1: mean not equal to 50

According to the H1, the mean can be greater than or less than 50. This is an example of a Two-tailed test.

In a similar manner, if H0: mean >=50, then H1: mean <50

Here the mean is less than 50. It is called a One-tailed test.

Type 1 and Type 2 Error

A hypothesis test can result in two types of errors.

Type 1 Error: A Type-I error occurs when sample results reject the null hypothesis despite being true.

Type 2 Error: A Type-II error occurs when the null hypothesis is not rejected when it is false, unlike a Type-I error.

Suppose a teacher evaluates the examination paper to decide whether a student passes or fails.

H0: Student has passed

H1: Student has failed

Type I error will be the teacher failing the student [rejects H0] although the student scored the passing marks [H0 was true]. 

Type II error will be the case where the teacher passes the student [do not reject H0] although the student did not score the passing marks [H1 is true].

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Practice Problems on Hypothesis Testing

Here are the practice problems on hypothesis testing that will help you understand how to apply these concepts in real-world scenarios:

A telecom service provider claims that customers spend an average of ₹400 per month, with a standard deviation of ₹25. However, a random sample of 50 customer bills shows a mean of ₹250 and a standard deviation of ₹15. Does this sample data support the service provider’s claim?

Solution: Let’s break this down:

• Null Hypothesis (H0): The average amount spent per month is ₹400.
• Alternate Hypothesis (H1): The average amount spent per month is not ₹400.
• Population Standard Deviation (σ): ₹25
• Sample Size (n): 50
• Sample Mean (x̄): ₹250

1. Calculate the z-value:

z=250-40025/50 −42.42

2. Compare with critical z-values: For a 5% significance level, critical z-values are -1.96 and +1.96. Since -42.42 is far outside this range, we reject the null hypothesis. The sample data suggests that the average amount spent is significantly different from ₹400.

Out of 850 customers, 400 made online grocery purchases. Can we conclude that more than 50% of customers are moving towards online grocery shopping?

Solution: Here’s how to approach it:

• Proportion of customers who shopped online (p): 400 / 850 = 0.47
• Null Hypothesis (H0): The proportion of online shoppers is 50% or more.
• Alternate Hypothesis (H1): The proportion of online shoppers is less than 50%.
• Sample Size (n): 850
• Significance Level (α): 5%

z=p-PP(1-P)/n

z=0.47-0.500.50.5/850  −1.74

2. Compare with the critical z-value: For a 5% significance level (one-tailed test), the critical z-value is -1.645. Since -1.74 is less than -1.645, we reject the null hypothesis. This means the data does not support the idea that most customers are moving towards online grocery shopping.

In a study of code quality, Team A has 250 errors in 1000 lines of code, and Team B has 300 errors in 800 lines of code. Can we say Team B performs worse than Team A?

Solution: Let’s analyze it:

• Proportion of errors for Team A (pA): 250 / 1000 = 0.25
• Proportion of errors for Team B (pB): 300 / 800 = 0.375
• Null Hypothesis (H0): Team B’s error rate is less than or equal to Team A’s.
• Alternate Hypothesis (H1): Team B’s error rate is greater than Team A’s.
• Sample Size for Team A (nA): 1000
• Sample Size for Team B (nB): 800

p=nApA+nBpBnA+nB

p=10000.25+8000.3751000+800 ≈ 0.305

z=​pA−pB​p(1-p)(1nA+1nB)

z=​0.25−0.375​0.305(1-0.305) (11000+1800) ≈ −5.72

2. Compare with the critical z-value: For a 5% significance level (one-tailed test), the critical z-value is +1.645. Since -5.72 is far less than +1.645, we reject the null hypothesis. The data indicates that Team B’s performance is significantly worse than Team A’s.

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Applications of Hypothesis Testing

Apart from the practical problems, let's look at the real-world applications of hypothesis testing across various fields:

Medicine and Healthcare

In medicine, hypothesis testing plays a pivotal role in assessing the success of new treatments. For example, researchers may want to find out if a new exercise regimen improves heart health. By comparing data from patients who followed the program to those who didn’t, they can determine if the exercise significantly improves health outcomes. Such rigorous testing allows medical professionals to rely on proven methods rather than assumptions.

Quality Control and Manufacturing

In manufacturing, ensuring product quality is vital, and hypothesis testing helps maintain those standards. Suppose a beverage company introduces a new bottling process and wants to verify if it reduces contamination. By analyzing samples from the new and old processes, hypothesis testing can reveal whether the new method reduces the risk of contamination. This allows manufacturers to implement improvements that enhance product safety and quality confidently.

Education and Learning

In education and learning, hypothesis testing is a tool to evaluate the impact of innovative teaching techniques. Imagine a situation where teachers introduce project-based learning to boost critical thinking skills. By comparing the performance of students who engaged in project-based learning with those in traditional settings, educators can test their hypothesis. The results can help educators make informed choices about adopting new teaching strategies.

Environmental Science

Hypothesis testing is essential in environmental science for evaluating the effectiveness of conservation measures. For example, scientists might explore whether a new water management strategy improves river health. By collecting and comparing data on water quality before and after the implementation of the strategy, they can determine whether the intervention leads to positive changes. Such findings are crucial for guiding environmental decisions that have long-term impacts.

Marketing and Advertising

In marketing, businesses use hypothesis testing to refine their approaches. For instance, a clothing brand might test if offering limited-time discounts increases customer loyalty. By running campaigns with and without the discount and analyzing the outcomes, they can assess if the strategy boosts customer retention. Data-driven insights from hypothesis testing enable companies to design marketing strategies that resonate with their audience and drive growth.

Limitations of Hypothesis Testing

Hypothesis testing has some limitations that researchers should be aware of:

• It cannot prove or establish the truth: Hypothesis testing provides evidence to support or reject a hypothesis, but it cannot confirm the absolute truth of the research question.
• Results are sample-specific: Hypothesis testing is based on analyzing a sample from a population, and the conclusions drawn are specific to that particular sample.
• Possible errors: During hypothesis testing, there is a chance of committing type I error (rejecting a true null hypothesis) or type II error (failing to reject a false null hypothesis).
• Assumptions and requirements: Different tests have specific assumptions and requirements that must be met to accurately interpret results.

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After reading this tutorial, you would have a much better understanding of hypothesis testing, one of the most important concepts in the field of Data Science . The majority of hypotheses are based on speculation about observed behavior, natural phenomena, or established theories.

If you are interested in statistics of data science and skills needed for such a career, you ought to explore the Post Graduate Program in Data Science.

1. What is hypothesis testing in statistics with example?

Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample data to draw conclusions about a population. It involves formulating two competing hypotheses, the null hypothesis (H0) and the alternative hypothesis (Ha), and then collecting data to assess the evidence. An example: testing if a new drug improves patient recovery (Ha) compared to the standard treatment (H0) based on collected patient data.

2. What is H0 and H1 in statistics?

In statistics, H0​ and H1​ represent the null and alternative hypotheses. The null hypothesis, H0​, is the default assumption that no effect or difference exists between groups or conditions. The alternative hypothesis, H1​, is the competing claim suggesting an effect or a difference. Statistical tests determine whether to reject the null hypothesis in favor of the alternative hypothesis based on the data.

3. What is a simple hypothesis with an example?

A simple hypothesis is a specific statement predicting a single relationship between two variables. It posits a direct and uncomplicated outcome. For example, a simple hypothesis might state, "Increased sunlight exposure increases the growth rate of sunflowers." Here, the hypothesis suggests a direct relationship between the amount of sunlight (independent variable) and the growth rate of sunflowers (dependent variable), with no additional variables considered.

4. What are the 3 major types of hypothesis?

The three major types of hypotheses are:

• Null Hypothesis (H0): Represents the default assumption, stating that there is no significant effect or relationship in the data.
• Alternative Hypothesis (Ha): Contradicts the null hypothesis and proposes a specific effect or relationship that researchers want to investigate.
• Nondirectional Hypothesis: An alternative hypothesis that doesn't specify the direction of the effect, leaving it open for both positive and negative possibilities.

5. What software tools can assist with hypothesis testing?

Several software tools offering distinct features can help with hypothesis testing. R and RStudio are popular for their advanced statistical capabilities. The Python ecosystem, including libraries like SciPy and Statsmodels, also supports hypothesis testing. SAS and SPSS are well-established tools for comprehensive statistical analysis. For basic testing, Excel offers simple built-in functions.

6. How do I interpret the results of a hypothesis test?

Interpreting hypothesis test results involves comparing the p-value to the significance level (alpha). If the p-value is less than or equal to alpha, you can reject the null hypothesis, indicating statistical significance. This suggests that the observed effect is unlikely to have occurred by chance, validating your analysis findings.

7. Why is sample size important in hypothesis testing?

Sample size is crucial in hypothesis testing as it affects the test’s power. A larger sample size increases the likelihood of detecting a true effect, reducing the risk of Type II errors. Conversely, a small sample may lack the statistical power needed to identify differences, potentially leading to inaccurate conclusions.

8. Can hypothesis testing be used for non-numerical data?

Yes, hypothesis testing can be applied to non-numerical data through non-parametric tests. These tests are ideal when data doesn't meet parametric assumptions or when dealing with categorical data. Non-parametric tests, like the Chi-square or Mann-Whitney U test, provide robust methods for analyzing non-numerical data and drawing meaningful conclusions.

9. How do I choose the proper hypothesis test?

Selecting the right hypothesis test depends on several factors: the objective of your analysis, the type of data (numerical or categorical), and the sample size. Consider whether you're comparing means, proportions, or associations, and whether your data follows a normal distribution. The correct choice ensures accurate results tailored to your research question.

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About the Author

Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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Hypothesis in Machine Learning

The concept of a hypothesis is fundamental in Machine Learning and data science endeavours. In the realm of machine learning, a hypothesis serves as an initial assumption made by data scientists and ML professionals when attempting to address a problem. Machine learning involves conducting experiments based on past experiences, and these hypotheses are crucial in formulating potential solutions.

It’s important to note that in machine learning discussions, the terms “hypothesis” and “model” are sometimes used interchangeably. However, a hypothesis represents an assumption, while a model is a mathematical representation employed to test that hypothesis. This section on “Hypothesis in Machine Learning” explores key aspects related to hypotheses in machine learning and their significance.

Table of Content

How does a Hypothesis work?

Hypothesis space and representation in machine learning, hypothesis in statistics, faqs on hypothesis in machine learning.

A hypothesis in machine learning is the model’s presumption regarding the connection between the input features and the result. It is an illustration of the mapping function that the algorithm is attempting to discover using the training set. To minimize the discrepancy between the expected and actual outputs, the learning process involves modifying the weights that parameterize the hypothesis. The objective is to optimize the model’s parameters to achieve the best predictive performance on new, unseen data, and a cost function is used to assess the hypothesis’ accuracy.

In most supervised machine learning algorithms, our main goal is to find a possible hypothesis from the hypothesis space that could map out the inputs to the proper outputs. The following figure shows the common method to find out the possible hypothesis from the Hypothesis space:

Hypothesis Space (H)

Hypothesis space is the set of all the possible legal hypothesis. This is the set from which the machine learning algorithm would determine the best possible (only one) which would best describe the target function or the outputs.

Hypothesis (h)

A hypothesis is a function that best describes the target in supervised machine learning. The hypothesis that an algorithm would come up depends upon the data and also depends upon the restrictions and bias that we have imposed on the data.

The Hypothesis can be calculated as:

[Tex]y = mx + b [/Tex]

• m = slope of the lines
• b = intercept

To better understand the Hypothesis Space and Hypothesis consider the following coordinate that shows the distribution of some data:

Say suppose we have test data for which we have to determine the outputs or results. The test data is as shown below:

We can predict the outcomes by dividing the coordinate as shown below:

So the test data would yield the following result:

But note here that we could have divided the coordinate plane as:

The way in which the coordinate would be divided depends on the data, algorithm and constraints.

• All these legal possible ways in which we can divide the coordinate plane to predict the outcome of the test data composes of the Hypothesis Space.
• Each individual possible way is known as the hypothesis.

Hence, in this example the hypothesis space would be like:

The hypothesis space comprises all possible legal hypotheses that a machine learning algorithm can consider. Hypotheses are formulated based on various algorithms and techniques, including linear regression, decision trees, and neural networks. These hypotheses capture the mapping function transforming input data into predictions.

Hypothesis Formulation and Representation in Machine Learning

Hypotheses in machine learning are formulated based on various algorithms and techniques, each with its representation. For example:

• Linear Regression : [Tex] h(X) = \theta_0 + \theta_1 X_1 + \theta_2 X_2 + … + \theta_n X_n[/Tex]
• Decision Trees : [Tex]h(X) = \text{Tree}(X)[/Tex]
• Neural Networks : [Tex]h(X) = \text{NN}(X)[/Tex]

In the case of complex models like neural networks, the hypothesis may involve multiple layers of interconnected nodes, each performing a specific computation.

Hypothesis Evaluation:

The process of machine learning involves not only formulating hypotheses but also evaluating their performance. This evaluation is typically done using a loss function or an evaluation metric that quantifies the disparity between predicted outputs and ground truth labels. Common evaluation metrics include mean squared error (MSE), accuracy, precision, recall, F1-score, and others. By comparing the predictions of the hypothesis with the actual outcomes on a validation or test dataset, one can assess the effectiveness of the model.

Hypothesis Testing and Generalization:

Once a hypothesis is formulated and evaluated, the next step is to test its generalization capabilities. Generalization refers to the ability of a model to make accurate predictions on unseen data. A hypothesis that performs well on the training dataset but fails to generalize to new instances is said to suffer from overfitting. Conversely, a hypothesis that generalizes well to unseen data is deemed robust and reliable.

The process of hypothesis formulation, evaluation, testing, and generalization is often iterative in nature. It involves refining the hypothesis based on insights gained from model performance, feature importance, and domain knowledge. Techniques such as hyperparameter tuning, feature engineering, and model selection play a crucial role in this iterative refinement process.

In statistics , a hypothesis refers to a statement or assumption about a population parameter. It is a proposition or educated guess that helps guide statistical analyses. There are two types of hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1 or Ha).

• Null Hypothesis(H 0 ): This hypothesis suggests that there is no significant difference or effect, and any observed results are due to chance. It often represents the status quo or a baseline assumption.
• Aternative Hypothesis(H 1 or H a ): This hypothesis contradicts the null hypothesis, proposing that there is a significant difference or effect in the population. It is what researchers aim to support with evidence.

Q. How does the training process use the hypothesis?

The learning algorithm uses the hypothesis as a guide to minimise the discrepancy between expected and actual outputs by adjusting its parameters during training.

Q. How is the hypothesis’s accuracy assessed?

Usually, a cost function that calculates the difference between expected and actual values is used to assess accuracy. Optimising the model to reduce this expense is the aim.

Q. What is Hypothesis testing?

Hypothesis testing is a statistical method for determining whether or not a hypothesis is correct. The hypothesis can be about two variables in a dataset, about an association between two groups, or about a situation.

Q. What distinguishes the null hypothesis from the alternative hypothesis in machine learning experiments?

The null hypothesis (H0) assumes no significant effect, while the alternative hypothesis (H1 or Ha) contradicts H0, suggesting a meaningful impact. Statistical testing is employed to decide between these hypotheses.

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How generative AI models can fuel scientific discovery

Using generative models to come up with new ideas, we can dramatically accelerate the pace at which we can discover new molecules, materials, drugs, and more.

Throughout history, humanity has made progress often through a combination of curiosity and creativity. When we have problems that need overcoming, we try to understand why something is the case to figure out a solution.

Many scientific discoveries were made as a result of trial and error. While methodical, this process can also be painstakingly slow. And in some fields of study, the impetus for solving problems can be extremely urgent, whether that’s developing new life-saving drugs, or finding new ways to mitigate the effects of climate change. It can take a decade to discover, test, and develop a new drug. In light of new realities like the COVID-19 pandemic, this is simply not fast enough.

We need to find new ways to spur our creativity and inspiration. No one person, or even a group of people, could possibly keep up with all the latest research in their field of study, let alone remember every iota of what they’ve read over their lifetimes. This, though, is an area where AI can greatly help us.

Today, there are already systems that can ingest large volumes data, sift through it, and help find patterns in the noise. And there are newer emerging streams of AI research that we work on that we believe can accelerate the pace of discovery even more. One of these areas is called generative models.

Generative models are a powerful tool in AI that’s crossed over into popular culture in recent years. We’ve seen AI tools that can mimic the styles of master painters, videos where an actor’s face is eerily plastered on a video of another actor, and AI systems where a user gives a prompt, for a picture or a short story, and they generate something entirely fictional based on the request.

These are the green shoots of the potential of generative models. They are probably our most powerful tool right now to leverage the vast troves of data in science and use it to come up with starting points to design and discover new materials, drugs and more, generate new knowledge, and create new solutions to challenging problems, including those related to climate, sustainability, healthcare and life sciences and more.

How generative models can accelerate the scientific method

In scientific discovery, we follow the scientific method — we start with a question, study it, come up with ideas, study some more, create a hypothesis, test it, assess the results, and report back. But in any discovery applications, there’s reams of information to potentially consume and understand to come up with an idea. Scientists can spend years working on a single question and not find an answer.

That’s partly a result of the limits in our knowledge, but it’s also because the space of possible answers is simply too large to systematically search. In just the field of drug discovery, it’s believed that there are some 10 63 possible drug-like molecules in the universe. Trial and error can’t possibly get us through all those combinations.

This is where generative models can be our creative aid and help us find new ideas that we might not have thought to consider before. It helps us break through the bottleneck in the process of idea generation and create new eureka moments.

All scientific discovery involves a hypothesis, and until now hypotheses have been exclusively developed by humans. But building AI systems that can learn from data and make novel and valuable suggestions can greatly aid augment human creativity, and drastically speed up the time it takes to find new ideas to test.

In just the field of drug discovery, it’s believed that there are some 10 63 possible drug-like molecules in the universe. Trial and error can’t possibly get us through all those combinations.

At IBM Research, we’ve been building a body of research exploring the development and application of generative models in discovery. Specifically, we created generative model-based AI systems to design molecules for a variety of materials discovery applications.

Our team developed one family of generative model algorithms that efficiently combines conditional generative models with reinforcement learning to design ligands 1 with desired activity against specific proteins and hit-like anticancer molecules 2 for specific omic profiles. We showed how generative models are able to support the initial design phases of the material discovery process and demonstrated how it can be combined with data-driven chemical synthesis planning to swiftly produce candidates for wet-lab experimentations.

Recently, my colleagues built a generative model that can propose new antimicrobial peptides 3 (AMPs) with desired properties. AMPs are viewed as a “drug of last resort” against antimicrobial resistance, one of the biggest threats to global health and food security. Our generative model identified novel candidate molecules, and a second AI system filtered them using predicted properties such as toxicity and broad-spectrum activity. In the span of a few weeks, we were able to identify several dozen novel candidate molecules — a process that can normally take years.

Similarly, another team at IBM Research used generative models, along with several other AI and high-performance computing advances, to come up with a new photoacid generator (PAG) — a material key to manufacturing semiconductors — a process that usually takes years and was completed in weeks.

Generative models, however, don’t have to be limited to just the hypothesis step of the scientific method. In the future, they can potentially help us figure out what questions we should even be asking before we try to find answers: Given everything we know about a field, what is the next question we should ask?

We can potentially create generative models to help us answer questions we don’t know where to start with either, such as how to find a new antiviral for an unknown protein, or whether we could make a catalyst for CO 2 in the atmosphere. We can potentially use generative models in testing, to help us determine what conditions we need to create for the most accurate results, and we can even use it to help us refine future tests after we’ve gotten our results.

Creating a scientific community of discovery

As part of our mission to accelerate discovery for IBM and its partners, we want to foster an open community around scientific discovery. Technologies like AI should be a tool that scientists and researchers use to carry out their research quicker and more effectively, rather than something that requires very specific domain knowledge to utilize.

To that end, we recently launched what we’re calling the Generative Toolkit for Scientific Discovery (GT4SD). It’s an open-source library (released under the MIT license) to accelerate hypothesis generation in the scientific discovery process that eases the adoption of state-of-the-art generative models. GT4SD includes models that can generate new molecule designs based on properties like target proteins, target omics profiles, scaffolds distances, binding energies, and additional targets relevant for materials and drug discovery.

GT4SD is an open-source library to accelerate hypothesis generation in the scientific discovery process that eases the adoption of state-of-the-art generative models.

The GT4SD library provides an effective environment for generating new hypotheses (or inference) and for fine-tuning generative models for specific domains using custom data sets (or retraining). It’s compatible with many popular deep learning frameworks, including PyTorch, PyTorch Lightning, HuggingFace Transformers, GuacaMol, and Moses. It serves a wide range of applications, ranging from materials science to drug discovery.

GT4SD’s common framework makes generative models easily accessible to a broad community, including AI/ML practitioners developing new generative models who want to deploy with just a few lines of code. GT4SD provides a centralized environment for scientists and students interested in using generative models in their scientific research, allowing them to access and explore a variety of different pretrained models. GT4SD provides consistent commands and interfaces for inference and retraining with customizable parameters across the different generative models.

The development of problem-specific intelligence is made possible by automatic workflows that allow for retraining with a user’s own data covering molecular structures and properties. The replacement of manual processes and human bias in the discovery process has important effects on applications that rely on generative models, leading to an acceleration of expert knowledge.

The entirety of GT4SD is available on GitHub , and we encourage you to try it out for yourself. In the near-term, we plan to continue expanding the toolkit’s portfolio and release new algorithms, frameworks and pre-trained models. It is our hope that through tools like GT4SD and partnerships, we can build an open community of discovery that together accelerates scientific discovery for urgent problems and speeds up the path for creating solutions that impact the world.

Learn more about:

Trustworthy Generation : Our methods facilitate data augmentation for trustworthy machine learning and accelerate novel designs for drug and material discovery, and beyond.

• John R Smith
• Matteo Manica
• Accelerated Discovery
• Generative AI
• Materials Discovery

Jannis Born et al . 2021. Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2 . Mach. Learn.: Sci. Technol . 2 025024 ↩

Jannis Born et al . PaccMann RL : De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning . iScience 24, 102269 April 23, 2021 ↩

Das, P., Sercu, T., Wadhawan, K. et al. Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations . Nat Biomed Eng 5, 613–623 (2021). ↩

How to Generate and Validate Product Hypotheses

Every product owner knows that it takes effort to build something that'll cater to user needs. You'll have to make many tough calls if you wish to grow the company and evolve the product so it delivers more value. But how do you decide what to change in the product, your marketing strategy, or the overall direction to succeed? And how do you make a product that truly resonates with your target audience?

There are many unknowns in business, so many fundamental decisions start from a simple "what if?". But they can't be based on guesses, as you need some proof to fill in the blanks reasonably.

Because there's no universal recipe for successfully building a product, teams collect data, do research, study the dynamics, and generate hypotheses according to the given facts. They then take corresponding actions to find out whether they were right or wrong, make conclusions, and most likely restart the process again.

On this page, we thoroughly inspect product hypotheses. We'll go over what they are, how to create hypothesis statements and validate them, and what goes after this step.

What Is a Hypothesis in Product Management?

A hypothesis in product development and product management is a statement or assumption about the product, planned feature, market, or customer (e.g., their needs, behavior, or expectations) that you can put to the test, evaluate, and base your further decisions on . This may, for instance, regard the upcoming product changes as well as the impact they can result in.

A hypothesis implies that there is limited knowledge. Hence, the teams need to undergo testing activities to validate their ideas and confirm whether they are true or false.

Hypotheses guide the product development process and may point at important findings to help build a better product that'll serve user needs. In essence, teams create hypothesis statements in an attempt to improve the offering, boost engagement, increase revenue, find product-market fit quicker, or for other business-related reasons.

It's sort of like an experiment with trial and error, yet, it is data-driven and should be unbiased . This means that teams don't make assumptions out of the blue. Instead, they turn to the collected data, conducted market research , and factual information, which helps avoid completely missing the mark. The obtained results are then carefully analyzed and may influence decision-making.

Such experiments backed by data and analysis are an integral aspect of successful product development and allow startups or businesses to dodge costly startup mistakes .

‍ When do teams create hypothesis statements and validate them? To some extent, hypothesis testing is an ongoing process to work on constantly. It may occur during various product development life cycle stages, from early phases like initiation to late ones like scaling.

In any event, the key here is learning how to generate hypothesis statements and validate them effectively. We'll go over this in more detail later on.

Idea vs. Hypothesis Compared

You might be wondering whether ideas and hypotheses are the same thing. Well, there are a few distinctions.

An idea is simply a suggested proposal. Say, a teammate comes up with something you can bring to life during a brainstorming session or pitches in a suggestion like "How about we shorten the checkout process?". You can jot down such ideas and then consider working on them if they'll truly make a difference and improve the product, strategy, or result in other business benefits. Ideas may thus be used as the hypothesis foundation when you decide to prove a concept.

A hypothesis is the next step, when an idea gets wrapped with specifics to become an assumption that may be tested. As such, you can refine the idea by adding details to it. The previously mentioned idea can be worded into a product hypothesis statement like: "The cart abandonment rate is high, and many users flee at checkout. But if we shorten the checkout process by cutting down the number of steps to only two and get rid of four excessive fields, we'll simplify the user journey, boost satisfaction, and may get up to 15% more completed orders".

A hypothesis is something you can test in an attempt to reach a certain goal. Testing isn't obligatory in this scenario, of course, but the idea may be tested if you weigh the pros and cons and decide that the required effort is worth a try. We'll explain how to create hypothesis statements next.

How to Generate a Hypothesis for a Product

The last thing those developing a product want is to invest time and effort into something that won't bring any visible results, fall short of customer expectations, or won't live up to their needs. Therefore, to increase the chances of achieving a successful outcome and product-led growth , teams may need to revisit their product development approach by optimizing one of the starting points of the process: learning to make reasonable product hypotheses.

If the entire procedure is structured, this may assist you during such stages as the discovery phase and raise the odds of reaching your product goals and setting your business up for success. Yet, what's the entire process like?

• It all starts with identifying an existing problem . Is there a product area that's experiencing a downfall, a visible trend, or a market gap? Are users often complaining about something in their feedback? Or is there something you're willing to change (say, if you aim to get more profit, increase engagement, optimize a process, expand to a new market, or reach your OKRs and KPIs faster)?
• Teams then need to work on formulating a hypothesis . They put the statement into concise and short wording that describes what is expected to achieve. Importantly, it has to be relevant, actionable, backed by data, and without generalizations.
• Next, they have to test the hypothesis by running experiments to validate it (for instance, via A/B or multivariate testing, prototyping, feedback collection, or other ways).
• Then, the obtained results of the test must be analyzed . Did one element or page version outperform the other? Depending on what you're testing, you can look into various merits or product performance metrics (such as the click rate, bounce rate, or the number of sign-ups) to assess whether your prediction was correct.
• Finally, the teams can make conclusions that could lead to data-driven decisions. For example, they can make corresponding changes or roll back a step.

How Else Can You Generate Product Hypotheses?

Such processes imply sharing ideas when a problem is spotted by digging deep into facts and studying the possible risks, goals, benefits, and outcomes. You may apply various MVP tools like (FigJam, Notion, or Miro) that were designed to simplify brainstorming sessions, systemize pitched suggestions, and keep everyone organized without losing any ideas.

Predictive product analysis can also be integrated into this process, leveraging data and insights to anticipate market trends and consumer preferences, thus enhancing decision-making and product development strategies. This approach fosters a more proactive and informed approach to innovation, ensuring products are not only relevant but also resonate with the target audience, ultimately increasing their chances of success in the market.

Besides, you can settle on one of the many frameworks that facilitate decision-making processes , ideation phases, or feature prioritization . Such frameworks are best applicable if you need to test your assumptions and structure the validation process. These are a few common ones if you're looking toward a systematic approach:

• Business Model Canvas (used to establish the foundation of the business model and helps find answers to vitals like your value proposition, finding the right customer segment, or the ways to make revenue);
• Lean Startup framework (the lean startup framework uses a diagram-like format for capturing major processes and can be handy for testing various hypotheses like how much value a product brings or assumptions on personas, the problem, growth, etc.);
• Design Thinking Process (is all about interactive learning and involves getting an in-depth understanding of the customer needs and pain points, which can be formulated into hypotheses followed by simple prototypes and tests).

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How to Make a Hypothesis Statement for a Product

Once you've indicated the addressable problem or opportunity and broken down the issue in focus, you need to work on formulating the hypotheses and associated tasks. By the way, it works the same way if you want to prove that something will be false (a.k.a null hypothesis).

If you're unsure how to write a hypothesis statement, let's explore the essential steps that'll set you on the right track.

Step 1: Allocate the Variable Components

Product hypotheses are generally different for each case, so begin by pinpointing the major variables, i.e., the cause and effect . You'll need to outline what you think is supposed to happen if a change or action gets implemented.

Put simply, the "cause" is what you're planning to change, and the "effect" is what will indicate whether the change is bringing in the expected results. Falling back on the example we brought up earlier, the ineffective checkout process can be the cause, while the increased percentage of completed orders is the metric that'll show the effect.

Make sure to also note such vital points as:

• what the problem and solution are;
• what are the benefits or the expected impact/successful outcome;
• which user group is affected;
• what are the risks;
• what kind of experiments can help test the hypothesis;
• what can measure whether you were right or wrong.

Step 2: Ensure the Connection Is Specific and Logical

Mind that generic connections that lack specifics will get you nowhere. So if you're thinking about how to word a hypothesis statement, make sure that the cause and effect include clear reasons and a logical dependency .

Think about what can be the precise and link showing why A affects B. In our checkout example, it could be: fewer steps in the checkout and the removed excessive fields will speed up the process, help avoid confusion, irritate users less, and lead to more completed orders. That's much more explicit than just stating the fact that the checkout needs to be changed to get more completed orders.

Step 3: Decide on the Data You'll Collect

Certainly, multiple things can be used to measure the effect. Therefore, you need to choose the optimal metrics and validation criteria that'll best envision if you're moving in the right direction.

If you need a tip on how to create hypothesis statements that won't result in a waste of time, try to avoid vagueness and be as specific as you can when selecting what can best measure and assess the results of your hypothesis test. The criteria must be measurable and tied to the hypotheses . This can be a realistic percentage or number (say, you expect a 15% increase in completed orders or 2x fewer cart abandonment cases during the checkout phase).

Once again, if you're not realistic, then you might end up misinterpreting the results. Remember that sometimes an increase that's even as little as 2% can make a huge difference, so why make 50% the merit if it's not achievable in the first place?

Step 4: Settle on the Sequence

It's quite common that you'll end up with multiple product hypotheses. Some are more important than others, of course, and some will require more effort and input.

Therefore, just as with the features on your product development roadmap , prioritize your hypotheses according to their impact and importance. Then, group and order them, especially if the results of some hypotheses influence others on your list.

Product Hypothesis Examples

To demonstrate how to formulate your assumptions clearly, here are several more apart from the example of a hypothesis statement given above:

• Adding a wishlist feature to the cart with the possibility to send a gift hint to friends via email will increase the likelihood of making a sale and bring in additional sign-ups.
• Placing a limited-time promo code banner stripe on the home page will increase the number of sales in March.
• Moving up the call to action element on the landing page and changing the button text will increase the click-through rate twice.
• By highlighting a new way to use the product, we'll target a niche customer segment (i.e., single parents under 30) and acquire 5% more leads. 

How to Validate Hypothesis Statements: The Process Explained

There are multiple options when it comes to validating hypothesis statements. To get appropriate results, you have to come up with the right experiment that'll help you test the hypothesis. You'll need a control group or people who represent your target audience segments or groups to participate (otherwise, your results might not be accurate).

‍ What can serve as the experiment you may run? Experiments may take tons of different forms, and you'll need to choose the one that clicks best with your hypothesis goals (and your available resources, of course). The same goes for how long you'll have to carry out the test (say, a time period of two months or as little as two weeks). Here are several to get you started.

Feedback and User Testing

Talking to users, potential customers, or members of your own online startup community can be another way to test your hypotheses. You may use surveys, questionnaires, or opt for more extensive interviews to validate hypothesis statements and find out what people think. This assumption validation approach involves your existing or potential users and might require some additional time, but can bring you many insights.

Conduct A/B or Multivariate Tests

One of the experiments you may develop involves making more than one version of an element or page to see which option resonates with the users more. As such, you can have a call to action block with different wording or play around with the colors, imagery, visuals, and other things.

To run such split experiments, you can apply tools like VWO that allows to easily construct alternative designs and split what your users see (e.g., one half of the users will see version one, while the other half will see version two). You can track various metrics and apply heatmaps, click maps, and screen recordings to learn more about user response and behavior. Mind, though, that the key to such tests is to get as many users as you can give the tests time. Don't jump to conclusions too soon or if very few people participated in your experiment.

Build Prototypes and Fake Doors

Demos and clickable prototypes can be a great way to save time and money on costly feature or product development. A prototype also allows you to refine the design. However, they can also serve as experiments for validating hypotheses, collecting data, and getting feedback.

For instance, if you have a new feature in mind and want to ensure there is interest, you can utilize such MVP types as fake doors . Make a short demo recording of the feature and place it on your landing page to track interest or test how many people sign up.

Usability Testing

Similarly, you can run experiments to observe how users interact with the feature, page, product, etc. Usually, such experiments are held on prototype testing platforms with a focus group representing your target visitors. By showing a prototype or early version of the design to users, you can view how people use the solution, where they face problems, or what they don't understand. This may be very helpful if you have hypotheses regarding redesigns and user experience improvements before you move on from prototype to MVP development.

You can even take it a few steps further and build a barebone feature version that people can really interact with, yet you'll be the one behind the curtain to make it happen. There were many MVP examples when companies applied Wizard of Oz or concierge MVPs to validate their hypotheses.

Or you can actually develop some functionality but release it for only a limited number of people to see. This is referred to as a feature flag , which can show really specific results but is effort-intensive. 

What Comes After Hypothesis Validation?

Analysis is what you move on to once you've run the experiment. This is the time to review the collected data, metrics, and feedback to validate (or invalidate) the hypothesis.

You have to evaluate the experiment's results to determine whether your product hypotheses were valid or not. For example, if you were testing two versions of an element design, color scheme, or copy, look into which one performed best.

It is crucial to be certain that you have enough data to draw conclusions, though, and that it's accurate and unbiased . Because if you don't, this may be a sign that your experiment needs to be run for some additional time, be altered, or held once again. You won't want to make a solid decision based on uncertain or misleading results, right?

• If the hypothesis was supported , proceed to making corresponding changes (such as implementing a new feature, changing the design, rephrasing your copy, etc.). Remember that your aim was to learn and iterate to improve.
• If your hypothesis was proven false , think of it as a valuable learning experience. The main goal is to learn from the results and be able to adjust your processes accordingly. Dig deep to find out what went wrong, look for patterns and things that may have skewed the results. But if all signs show that you were wrong with your hypothesis, accept this outcome as a fact, and move on. This can help you make conclusions on how to better formulate your product hypotheses next time. Don't be too judgemental, though, as a failed experiment might only mean that you need to improve the current hypothesis, revise it, or create a new one based on the results of this experiment, and run the process once more.

On another note, make sure to record your hypotheses and experiment results . Some companies use CRMs to jot down the key findings, while others use something as simple as Google Docs. Either way, this can be your single source of truth that can help you avoid running the same experiments or allow you to compare results over time.

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Final Thoughts on Product Hypotheses

The hypothesis-driven approach in product development is a great way to avoid uncalled-for risks and pricey mistakes. You can back up your assumptions with facts, observe your target audience's reactions, and be more certain that this move will deliver value.

However, this only makes sense if the validation of hypothesis statements is backed by relevant data that'll allow you to determine whether the hypothesis is valid or not. By doing so, you can be certain that you're developing and testing hypotheses to accelerate your product management and avoiding decisions based on guesswork.

Certainly, a failed experiment may bring you just as much knowledge and findings as one that succeeds. Teams have to learn from their mistakes, boost their hypothesis generation and testing knowledge , and make improvements according to the results of their experiments. This is an ongoing process, of course, as no product can grow if it isn't iterated and improved.

If you're only planning to or are currently building a product, Upsilon can lend you a helping hand. Our team has years of experience providing product development services for growth-stage startups and building MVPs for early-stage businesses , so you can use our expertise and knowledge to dodge many mistakes. Don't be shy to contact us to discuss your needs! 

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