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Introduction To Hypothesis Testing
Published by ฐิติภรณ์ พันธุเมธา Modified over 5 years ago
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Presentation on theme: "Introduction To Hypothesis Testing"— Presentation transcript:
Anthony Greene1 Simple Hypothesis Testing Detecting Statistical Differences In The Simplest Case: and are both known I The Logic of Hypothesis Testing:
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This document provides an overview of hypothesis testing including: - Defining null and alternative hypotheses - Types of errors like Type I and Type II - Test statistics and significance levels for comparing means, proportions, and standard deviations of one and two populations - Examples are given for hypothesis tests on population means ...
It explains the basic steps in hypothesis testing as testing a claim about a population parameter by collecting a sample, determining the appropriate test statistic based on the sampling distribution, and comparing it to critical values to reject or fail to reject the null hypothesis.
Hypothesis Testing. Step 1. • Before running any analysis, VIZUALIZE! Visualize! Anatomy of a statistical test. • If your change had no effect, what would the world look. like? No difference in means. No slope in relationship. • This is known as the null hypothesis. Anatomy of a statistical test.
This document discusses hypothesis testing, including: 1) The objectives are to formulate statistical hypotheses, discuss types of errors, establish decision rules, and choose appropriate tests. 2) Key symbols and concepts are defined, such as the null and alternative hypotheses, Type I and Type II errors, test statistics like z and t, means ...
The hypothesis testing framework. Start with two hypotheses about the population: the null hypothesis and the alternative hypothesis. Choose a sample, collect data, and analyze the data. Figure out how likely it is to see data like what we got/observed, IF the null hypothesis were true.
1 INTRODUCTION TO HYPOTHESIS TESTING. From R. B. McCall, Fundamental Statistics for Behavioral Sciences, 5th edition, Harcourt Brace Jovanovich Publishers, New York 1990. 2 OUTLINE Population parameters - sample statistics.
Table 9.1 9-* Typical Values Usually set to a low value So there is a small chance of rejecting a true H0 Typically, = 0.05 Strong evidence is required to reject H0 Usually choose α between 0.01 and 0.05 = 0.01 requires very strong evidence to reject H0 Tradeoff between and β For fixed sample size, the lower , the higher β And the higher ...
Steps for hypothesis testing are: Restate the question as a research hypothesis and a null hypothesis about the population. Determine the characteristics of the comparison distribution. Determine the cutoff sample score on the comparison distribution at which the null hypothesis should be rejected.
It discusses the key steps in hypothesis testing, including formulating the null and alternative hypotheses, determining the significance level, calculating the test statistic, defining the critical region, and interpreting the results.
This document provides an overview of statistical inference and hypothesis testing. It discusses key concepts like point estimation, confidence intervals, sample means, and hypothesis testing errors. Specific hypothesis tests covered include tests for a mean (using z-tests and t-tests) and tests for a proportion.