Sampling Distributions and Hypothesis Testing

Flashcards covering key concepts related to sampling distributions, standard error, the central limit theorem, and the steps in hypothesis testing.

Sampling Distribution

A probability distribution of a statistic obtained by selecting all possible samples of a specific size from a population.

Standard Error of the Mean (SEOM)

The standard deviation of the sampling distribution of the mean, calculated as the standard deviation of the raw-score population divided by the square root of N.

Central Limit Theorem

A theorem stating that the sampling distribution of the mean approaches a normal distribution as the sample size (N) increases, regardless of the shape of the population distribution.

Z-Score

A measure of how many standard deviations an element is from the mean; used in hypothesis testing to determine the likelihood of a sample mean.

P-Value

The probability of obtaining an observed result, or a more extreme one, when the null hypothesis is true.

Alpha (α)

The threshold probability level used in hypothesis testing to determine whether to reject the null hypothesis.

Sampling Variability

The natural variation in sample statistics that occurs due to different sample selections from the same population.

Hypothesis Testing Steps

A systematic process that includes calculating the standard error, determining the Z-Score, finding the P-Value, comparing it to Alpha, and making a decision regarding the null hypothesis.

Distribution of Sample Means

The distribution obtained by calculating the means from all possible samples of a fixed size drawn from a population.