Hypothesis Testing

9. Hypothesis Testing#

This chapter develops inferential thinking through hypothesis tests. You will compare observed evidence with chance models and translate statistical results into cautious management decisions.

Chapter Video

Video credit: CrashCourse. (2018, June 27). How P-Values Help Us Test Hypotheses: Crash Course Statistics #21. YouTube. Watch on YouTube.

This embedded video is provided for educational reference only. All rights are retained by the respective copyright holder.

Learning Objectives

By the end of this chapter, you will be able to:

State null and alternative hypotheses for a data question.
Choose a test statistic that measures the relevant difference.
Use simulation or probability to approximate a null distribution.
Interpret p-values as evidence against a chance model.
Make decisions while separating statistical evidence from practical importance.

Chapter Flow

Assessing Model 1

Assessing Model 2

Hypotheses and p-Value

Glossary

#	Term	Explanation
1	Hypothesis test	A method for comparing observed data with a stated chance model.
2	Null hypothesis	The baseline claim tested against the data.
3	Alternative hypothesis	The competing claim suggested by the research question.
4	Test statistic	A numeric measure used to summarize evidence.
5	Observed statistic	The test statistic computed from the actual data.
6	Null distribution	The distribution of a test statistic assuming the null hypothesis is true.
7	P-value	The probability, under the null model, of evidence at least as extreme as observed.
8	Significance level	A cutoff used to decide whether a p-value is small enough to reject the null.
9	Reject the null	Conclude that the observed evidence is inconsistent with the null model.
10	Fail to reject	Decide that the data do not provide enough evidence against the null model.
11	One-sided test	A test where the alternative points in one direction.
12	Two-sided test	A test where the alternative allows differences in either direction.
13	Simulation	Repeated random trials used to approximate a distribution.
14	Random assignment	Assigning units to groups by chance.
15	Chance model	A model describing what outcomes would look like under randomness alone.
16	Statistical significance	Evidence judged unlikely under the null model.
17	Practical significance	The real-world size or importance of an effect.
18	Type I error	Rejecting a true null hypothesis.
19	Type II error	Failing to reject a false null hypothesis.
20	Inference	Using sample data to reason about a broader process.
21	Decision rule	A rule for converting statistical evidence into an action.