9.1 Null and Alternative Hypotheses
The actual test begins by considering two hypotheses . They are called the null hypothesis and the alternative hypothesis . These hypotheses contain opposing viewpoints.
H 0 , the — null hypothesis: a statement of no difference between sample means or proportions or no difference between a sample mean or proportion and a population mean or proportion. In other words, the difference equals 0.
H a —, the alternative hypothesis: a claim about the population that is contradictory to H 0 and what we conclude when we reject H 0 .
Since the null and alternative hypotheses are contradictory, you must examine evidence to decide if you have enough evidence to reject the null hypothesis or not. The evidence is in the form of sample data.
After you have determined which hypothesis the sample supports, you make a decision. There are two options for a decision. They are reject H 0 if the sample information favors the alternative hypothesis or do not reject H 0 or decline to reject H 0 if the sample information is insufficient to reject the null hypothesis.
Mathematical Symbols Used in H 0 and H a :
H 0 always has a symbol with an equal in it. H a never has a symbol with an equal in it. The choice of symbol depends on the wording of the hypothesis test. However, be aware that many researchers use = in the null hypothesis, even with > or < as the symbol in the alternative hypothesis. This practice is acceptable because we only make the decision to reject or not reject the null hypothesis.
Example 9.1
H 0 : No more than 30 percent of the registered voters in Santa Clara County voted in the primary election. p ≤ 30 H a : More than 30 percent of the registered voters in Santa Clara County voted in the primary election. p > 30
A medical trial is conducted to test whether or not a new medicine reduces cholesterol by 25 percent. State the null and alternative hypotheses.
Example 9.2
We want to test whether the mean GPA of students in American colleges is different from 2.0 (out of 4.0). The null and alternative hypotheses are the following: H 0 : μ = 2.0 H a : μ ≠ 2.0
We want to test whether the mean height of eighth graders is 66 inches. State the null and alternative hypotheses. Fill in the correct symbol (=, ≠, ≥, <, ≤, >) for the null and alternative hypotheses.
- H 0 : μ __ 66
- H a : μ __ 66
Example 9.3
We want to test if college students take fewer than five years to graduate from college, on the average. The null and alternative hypotheses are the following: H 0 : μ ≥ 5 H a : μ < 5
We want to test if it takes fewer than 45 minutes to teach a lesson plan. State the null and alternative hypotheses. Fill in the correct symbol ( =, ≠, ≥, <, ≤, >) for the null and alternative hypotheses.
- H 0 : μ __ 45
- H a : μ __ 45
Example 9.4
An article on school standards stated that about half of all students in France, Germany, and Israel take advanced placement exams and a third of the students pass. The same article stated that 6.6 percent of U.S. students take advanced placement exams and 4.4 percent pass. Test if the percentage of U.S. students who take advanced placement exams is more than 6.6 percent. State the null and alternative hypotheses. H 0 : p ≤ 0.066 H a : p > 0.066
On a state driver’s test, about 40 percent pass the test on the first try. We want to test if more than 40 percent pass on the first try. Fill in the correct symbol (=, ≠, ≥, <, ≤, >) for the null and alternative hypotheses.
- H 0 : p __ 0.40
- H a : p __ 0.40
Collaborative Exercise
Bring to class a newspaper, some news magazines, and some internet articles. In groups, find articles from which your group can write null and alternative hypotheses. Discuss your hypotheses with the rest of the class.
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Alternative hypothesis
by Marco Taboga , PhD
In a statistical test, observed data is used to decide whether or not to reject a restriction on the data-generating probability distribution.
The assumption that the restriction is true is called null hypothesis , while the statement that the restriction is not true is called alternative hypothesis.
A correct specification of the alternative hypothesis is essential to decide between one-tailed and two-tailed tests.
Table of contents
Mathematical setting
Choice between one-tailed and two-tailed tests, the critical region, the interpretation of the rejection, the interpretation must be coherent with the alternative hypothesis.
- Power function
Accepting the alternative
More details, keep reading the glossary.
In order to fully understand the concept of alternative hypothesis, we need to remember the essential elements of a statistical inference problem:
we observe a sample drawn from an unknown probability distribution;
in principle, any valid probability distribution could have generated the sample;
however, we usually place some a priori restrictions on the set of possible data-generating distributions;
A couple of simple examples follow.
When we conduct a statistical test, we formulate a null hypothesis as a restriction on the statistical model.
The alternative hypothesis is
The alternative hypothesis is used to decide whether a test should be one-tailed or two-tailed.
The null hypothesis is rejected if the test statistic falls within a critical region that has been chosen by the statistician.
The critical region is a set of values that may comprise:
only the left tail of the distribution or only the right tail (one-tailed test);
both the left and the right tail (two-tailed test).
The choice of the critical region depends on the alternative hypothesis. Let us see why.
The interpretation is different depending on the tail of the distribution in which the test statistic falls.
The choice between a one-tailed or a two-tailed test needs to be done in such a way that the interpretation of a rejection is always coherent with the alternative hypothesis.
When we deal with the power function of a test, the term "alternative hypothesis" has a special meaning.
We conclude with a caveat about the interpretation of the outcome of a test of hypothesis.
The interpretation of a rejection of the null is controversial.
According to some statisticians, rejecting the null is equivalent to accepting the alternative.
However, others deem that rejecting the null does not necessarily imply accepting the alternative. In fact, it is possible to think of situations in which both hypotheses can be rejected. Let us see why.
According to the conceptual framework illustrated by the images above, there are three possibilities:
the null is true;
the alternative is true;
neither the null nor the alternative is true because the true data-generating distribution has been excluded from the statistical model (we say that the model is mis-specified).
If we are in case 3, accepting the alternative after a rejection of the null is an incorrect decision. Moreover, a second test in which the alternative becomes the new null may lead us to another rejection.
You can find more details about the alternative hypothesis in the lecture on Hypothesis testing .
Previous entry: Almost sure
Next entry: Binomial coefficient
How to cite
Please cite as:
Taboga, Marco (2021). "Alternative hypothesis", Lectures on probability theory and mathematical statistics. Kindle Direct Publishing. Online appendix. https://www.statlect.com/glossary/alternative-hypothesis.
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IMAGES
VIDEO
COMMENTS
The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test: Null hypothesis (H0): There’s no effect in the population. Alternative hypothesis (Ha or H1): There’s an effect in the population.
Examples of Alternative Hypotheses. The following examples illustrate how to define the null and alternative hypotheses for different research problems. Example 1: A biologist wants to test if the mean weight of a certain population of turtle is different from the widely-accepted mean weight of 300 pounds.
What is an alternative hypothesis? The alternative hypothesis (H A) is the other answer to your research question. It claims that there’s an effect in the population. Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true. The alternative hypothesis is ...
H a —, the alternative hypothesis: a claim about the population that is contradictory to H 0 and what we conclude when we reject H 0. Since the null and alternative hypotheses are contradictory, you must examine evidence to decide if you have enough evidence to reject the null hypothesis or not.
In a statistical test, observed data is used to decide whether or not to reject a restriction on the data-generating probability distribution. The assumption that the restriction is true is called null hypothesis, while the statement that the restriction is not true is called alternative hypothesis. A correct specification of the alternative ...
In statistics, alternative hypothesis is often denoted as Ha or H1. Hypotheses are formulated to compare in a statistical hypothesis test. In the domain of inferential statistics, two rival hypotheses can be compared by explanatory power and predictive power.
a statement about the value of a population parameter, in case of two hypotheses, the statement assumed to be true is called the null hypothesis (notation \(H_{0}\)) and the contradictory statement is called the alternative hypothesis (notation \(H_{a}\)).
The alternative hypothesis is simply the reverse of the null hypothesis, and there are three options, depending on where we expect the difference to lie. Thus, our alternative hypothesis is the mathematical way of stating our research question.
The alternative hypothesis, therefore, can be written with the population symbols to indicate that the researcher expects the hypothesis to be true beyond the sample. However, the alternative hypothesis can also be written with sample symbols to reiterate that the data used to test it are from samples and can only be used to estimate the ...
The alternative hypothesis is one of two mutually exclusive hypotheses in a hypothesis test. The alternative hypothesis states that a population parameter does not equal a specified value. Typically, this value is the null hypothesis value associated with no effect, such as zero.