A statistical hypothesis test is a method of statistical inference used to decide whether the sample data sufficiently supports a particular hypothesis about a population parameter. We typically consider two competing hypotheses:

  1. The null hypothesis (): This is the default assumption or the status quo. It claims that the effect being studied does not exist
  2. The alternative hypothesis ( or ): This is the hypothesis we’re testing against the null hypothesis.

The process involves collecting sample data and using it to determine whether there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis .

The usage of hypothesis testing and p-value in particular is ubiquitous in scientific research. However, it is also controversial and are under heavy debate within the statistical and metascience community.

Subtopics

Test Methods

Quantitative Variables

  • Z-test - approximate quantitative variables with normal distribution
  • Student’s T-test - approximate quantitative variables with t-distribution

Qualitative Variables

Assumptions Tests

Steps

When perform hypothesis testing, we typically have the following steps:

  1. Formulate research questions and develop hypotheses
  2. Design the study and collect data
  3. Choose an appropriate statistical test with an appropriate test statistic
  4. Verify that the data meet the assumptions for the chosen test
  5. Weigh up evidence against hypotheses
  6. Draw conclusion
  • Decide whether to reject or retain the null hypothesis