A type of statistical analysis in which you put your assumptions about a population parameter to the test.
- Null Hypothesis (H₀): The statement we aim to disprove, typically that there is no difference between groups.
- Alternative Hypothesis (H₁): The opposite of the null hypothesis, what we hope to prove.
- P-value: The probability of observing a result at least as extreme as the one we obtained, assuming the null hypothesis is true. A low p-value suggests rejecting the null hypothesis.
- Type I Error (Alpha): The probability of rejecting a true null hypothesis.
- Type II Error (Beta): The probability of failing to reject a false null hypothesis.
- Chi-Square Test: A statistical test used to determine if there is a significant association between two categorical variables.The test compares the observed values in your data to the expected values that you would see if the null hypothesis is true.
