Hypothesis Testing: The One-, and Two-Sample Case

These flashcards cover key concepts related to hypothesis testing, including types of tests, hypotheses, significance levels, and error types.

t-test

A statistical test based on the Student’s t-distribution, used to compare group means.

z-test

A statistical test used when the population standard deviation is known.

student's t-distribution

A family of distributions used in t-tests, characterized by their dependence on the sample size.

null hypothesis (H0)

The hypothesis that there is no effect or no difference; it is the hypothesis that researchers aim to test.

alternative hypothesis (H1)

The hypothesis that there is an effect or a difference; it contrasts the null hypothesis.

alpha level (α)

The threshold for statistical significance; commonly set at 0.05.

rejection rule

A guideline that dictates whether to reject the null hypothesis based on calculated values falling beyond critical values.

degrees of freedom (df)

The number of independent values or quantities which can be assigned to a statistical distribution.

paired t-test

A t-test used to compare means from the same group at different times or under different conditions.

independent samples

Two samples that are taken from separate groups of subjects, used in an independent t-test.

significance level (p-value)

The probability of committing a Type I error, indicating whether results are statistically significant.

standard error (SE)

An estimate of the variability of a sample mean from the population mean.

critical value

The threshold value that the test statistic must exceed in order to reject the null hypothesis.

variance

A measure of how much values in a dataset differ from the mean; used in the calculation of standard deviation.

one-tailed test

A hypothesis test where the alternative hypothesis specifies a direction of the difference.

two-tailed test

A hypothesis test where the alternative hypothesis does not specify a direction; tests for differences in both directions.

Type I error

The error made when rejecting a true null hypothesis.

Type II error

The error made when failing to reject a false null hypothesis.