Power and Sampling Distributions

These flashcards cover key concepts related to power, sampling distributions, and statistical theories.

Power

The probability of correctly rejecting the null hypothesis when it is false.

Sampling Distributions

The distribution of sample means for all possible random samples of a particular size from a population.

Central Limit Theorem

The theorem stating that the distribution of sample means will be approximately normal if the sample size is large enough (usually n ≥ 30) or if the population is normally distributed.

Standard Error of M

The standard deviation of the distribution of sample means, measuring how well an individual sample mean represents the true population mean.

Expected Value of M

The mean of the distribution of sample means, equal to the population mean (𝜇).

Normal Distribution

A bell-shaped distribution that is symmetrical about the mean, where most of the observations cluster around the central peak.

Bimodal Distribution

A distribution with two different modes or peaks.

Skewed Distribution

A distribution that is not symmetrical and has a tail on one side longer than the other.

Uniform Distribution

A distribution in which all outcomes occur with equal probability.

Probability of a Sample Mean

The likelihood that a calculated sample mean falls within a specified range of values based on the population distribution.

Z-score

The number of standard deviations a data point is from the mean, used to determine probabilities in a normal distribution.

Population Mean (𝜇)

The average of all values in a population.

Population Standard Deviation (𝜎)

A measure of the dispersion of a set of data from its mean in a population.

Sample Size (n)

The number of observations in a sample drawn from a population.

Distribution of Sample Means

A theoretical distribution of possible sample means that shows how sample means are distributed around the population mean.