z = (p̂ - p₀) / √(p₀(1-p₀)/n), where p̂ is the sample proportion, p₀ is the hypothesized population proportion, and n is the sample size.

t = (x̄ - μ) / (s/√n), where x̄ is the sample mean, μ is the population mean, s is the sample standard deviation, and n is the sample size.

Statistical significance indicates that the observed result from a sample is unlikely to have occurred by chance alone if the null hypothesis were true, usually determined by comparing the p-value to the significance level (alpha).

If the p-value is less than or equal to the significance level (α), we reject the null hypothesis. If the p-value is greater than α, we fail to reject the null hypothesis.

Including context in the conclusion connects the statistical results back to the real-world scenario, making the conclusion meaningful and interpretable in the context of the problem.

A large z-score (typically > 2 or < -2) indicates that the sample statistic is far from what is expected under the null hypothesis, providing evidence to reject the null hypothesis.

The z-score represents the number of standard deviations a data point is from the mean of the distribution.

Rejecting: Sufficient evidence to say H₀ is likely false. | Failing to reject: Not enough evidence to reject H₀; does NOT mean H₀ is true.