Statistics and Probability · Hypothesis testing

Hypothesis testing basics

Checking whether observed data is surprising enough to reject a specific default assumption, the null hypothesis.

Also known as: null hypothesis, p-value, Type I and Type II error.

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• Confidence intervals: A range, instead of a single number, built to contain the true value a stated percentage of the time.

Leads to

• t-tests: Comparing one or two sample means against a hypothesized value, when the true variance is unknown.
• Chi-squared tests: Checking whether observed category counts match an expected pattern, or whether two categorical variables are related.

Related topics

• t-tests: Comparing one or two sample means against a hypothesized value, when the true variance is unknown.
• Chi-squared tests: Checking whether observed category counts match an expected pattern, or whether two categorical variables are related.