The following material is derived from Florida State College at Jacksonville Library and Learning Commons ^[1] and is used under a Creative Commons Attribution 4.0 International Licence.

What is Hypothesis Testing?

A statistical hypothesis is an assumption about a population parameter. This assumption may or may not be true. Hypothesis testing refers to the formal procedures used by statisticians to reject or not reject statistical hypotheses.

Statistical Hypotheses

The best way to determine whether a statistical hypothesis is true would be to examine the entire population. Since that is often impractical, researchers typically examine a random sample from the population. If sample data are not consistent with the statistical hypothesis, the hypothesis is rejected.

There are two types of statistical hypotheses.

• Null hypothesis. The null hypothesis, denoted by H₀, is usually the hypothesis that sample observations result purely from chance.
• Alternative hypothesis. The alternative hypothesis, denoted by H₁ or H_a, is the hypothesis that sample observations are influenced by some non-random cause.

Steps in Hypothesis Tests

Statisticians follow a formal process to determine whether to reject a null hypothesis, based on sample data. This process, called hypothesis testing, consists of four steps.

Step 1: State the hypotheses. This involves stating the null and alternative hypotheses. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false.

Example:

Null Hypothesis (H₀): There is no association between gender and brand preferences

Alternative Hypothesis (H₁): There is an association between gender and brand preferences

Step 2: State the alpha value and the decision rule

For example: the alpha value is usually set at 0.05;

Example: if the p-value is less than 0.05, then reject H₀

Step 3: Choose the appropriate statistical test

Examples: Chi-square, Correlation, One-sample t-test, Independent samples t-test, ANOVA

Step 4: Interpret results. Discuss implications for managers