Hypothesis testing is a method of statistical inference used to determine if there is enough evidence in a sample to conclude that a certain condition is true for the entire population.

• Null Hypothesis (H₀): The assumption that there is no effect or no difference.
• Alternative Hypothesis (H₁): The assumption that there is a significant effect or difference.

• Type I Error: Rejecting a true null hypothesis (false positive).
• Type II Error: Failing to reject a false null hypothesis (false negative).

The test statistic is a standardized value that is calculated from sample data during a hypothesis test.

\[ Z = \frac{\bar{X} - \mu}{\frac{\sigma}{\sqrt{n}}} \]

The p-value is the probability of observing test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct.

Confidence intervals show the range in which we expect the true population parameter to lie.