Point estimate ± (Critical value) * (Standard error)

df = n - 2, where n is the sample size.

$t = \frac{\bar{x} - \mu_0}{s/\sqrt{n}}$

$z = \frac{\hat{p} - p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}}$

$\chi^2 = \sum \frac{(Observed - Expected)^2}{Expected}$

One Sample T-Test: Compares the mean of a single sample to a known value. | Two Sample T-Test: Compares the means of two independent samples to determine if there is a significant difference.

Chi-Square Test for Independence: Tests for association between two categorical variables within a single population. | Chi-Square Test for Homogeneity: Tests if the distribution of a categorical variable is the same across multiple populations.

One Proportion Z-Test: Tests a claim about a single population proportion. | Two Proportion Z-Test: Compares the proportions of two independent populations.

T-tests: Used when the population standard deviation is unknown and estimated from the sample. | Z-tests: Used when the population standard deviation is known.

Confidence Interval: Estimates a population parameter with a range of plausible values. | Hypothesis Test: Assesses evidence against a specific claim about a population parameter.

A statistical test used to test a claim about a single population proportion.

A statistical test used to test a claim about a single population mean when the population standard deviation is unknown.

A statistical test used to determine if there is a significant association between two categorical variables.

A statistical test used to determine if there is a significant linear relationship between two quantitative variables.

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