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.

A statement that there is no effect or no difference; the hypothesis we are trying to disprove.

The hypothesis that contradicts the null hypothesis; it represents what we are trying to find evidence for.

The probability of observing a test statistic as extreme as, or more extreme than, the one computed, assuming the null hypothesis is true.

The probability of rejecting the null hypothesis when it is actually true (Type I error).

To conclude that there is sufficient evidence to suggest the null hypothesis is false.

To conclude that there is not enough evidence to reject the null hypothesis; it does NOT mean the null hypothesis is true.