PSYC 200 Vocab Quiz 2

Sampling Error

The difference between a sample statistic and the corresponding population parameter. For a sample mean, sampling error is calculated as M − μ. It occurs naturally because a sample usually does not perfectly represent the population.

Sampling Distribution

The distribution of statistics obtained from all possible random samples of a specific size selected from a population.

Distribution of Sample Means

The sampling distribution created by calculating the mean for every possible sample of a specific size from a population. It shows how sample means vary from sample to sample.

Central Limit Theorem (CLT)

The principle stating that the distribution of sample means becomes approximately normal as the sample size increases, even when the original population is not normally distributed. The mean of the sample means equals the population mean, and the standard error decreases as sample size increases.

Expected Value of M

The mean of the distribution of sample means. The expected value of the sample mean is equal to the population mean

Standard Error of M

The standard deviation of the distribution of sample means. It measures how much sample means typically vary from the population mean. The formula is σM = σ/√n.

Law of Large Numbers

The principle stating that as sample size increases, the sample mean tends to get closer to the population mean. Larger samples generally produce smaller sampling errors.

Hypothesis testing

Theprocess of using sample data to evaluate a claim about a population and decide whether there is enough evidence to reject the null hypothesis.

Null hypothesis (H₀)

The statement that there is no effect, no difference, or no relationship in the population; it is the hypothesis tested statistically.

Level of significance

The criterion used to determine whether a result is statistically significant; it represents the maximum probability of making a Type I error.

Alpha level (α)

The probability of rejecting a true null hypothesis. Common alpha levels are .05 and .01.

Critical region

The range of test-statistic values that leads to rejection of the null hypothesis. The critical region is determined by the alpha level and the type of test.

Test statistic

A value calculated from sample data that is used to determine whether the null hypothesis should be rejected.

Type I error

Rejecting the null hypothesis when it is actually true; a false positive.

Type II error

Failing to reject the null hypothesis when it is actually false; a false negative.

Beta (β)

The probability of making a Type II error.

Significant

A result is statistically significant when it is sufficiently unlikely under the null hypothesis to justify rejecting the null hypothesis, based on the chosen alpha level.

Directional test

A hypothesis test that predicts the specific direction of an effect or difference, such as greater than or less than.

One-tailed test

A hypothesis test in which the critical region is located entirely in one tail of the sampling distribution because the alternative hypothesis is directional.

Effect size

A numerical measure of the magnitude or size of a difference, relationship, or treatment effect, independent of whether it is statistically significant.

Cohen’s d

A standardized measure of effect size for the difference between two means, calculated as the mean difference divided by the standard deviation.

Power

The probability that a statistical test will correctly reject a false null hypothesis; power equals 1 − β.