Standard Deviation: Measures the spread of data around the mean, in original units. | Variance: Measures the average squared distance from the mean, in squared units.

One-Sample: Compares a sample proportion to a known population proportion. | Two-Sample: Compares the proportions of two independent samples.

Central Limit Theorem: Applies to means (quantitative data), stating that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases. | Large Counts Condition: Applies to proportions (categorical data), ensuring the sampling distribution of the sample proportion is approximately normal.

Point Estimate: A single value used to estimate a population parameter. | Confidence Interval: A range of values likely to contain the population parameter, providing a measure of uncertainty.

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

$$\sqrt{\frac{p_1(1-p_1)}{n_1} + \frac{p_2(1-p_2)}{n_2}}$$

$$\mu = p_1 - p_2$$

Point Estimate ± (Critical Value * Standard Error)

$$\hat{p} = \frac{number \ of \ successes}{sample \ size}$$

$$\sqrt{\frac{\hat{p_1}(1-\hat{p_1})}{n_1} + \frac{\hat{p_2}(1-\hat{p_2})}{n_2}}$$

Distribution of all possible differences in sample proportions you could get if you repeated the study many times.

The standard deviation of a sampling distribution.

A single value estimate for a population parameter.

Bias that occurs when certain groups are more or less likely to respond to a survey.

Condition to confirm normality for proportion inference, verifying that $n_1p_1 ge 10$, $n_1(1 - p_1) ge 10$, $n_2p_2 ge 10$, and $n_2(1 - p_2) ge 10$.