LECTURE 3: ONE-SAMPLE T-TEST

Flashcards covering key vocabulary and concepts related to one-sample t-tests, hypothesis testing, and statistical analysis, based on lecture notes.

Degrees of freedom

Number of values that can vary in the dataset.

P-values

Probability of observing results if the null hypothesis is true.

Effect sizes

Absolute magnitude of the difference between groups.

Shapiro-Wilk test

Used to check for a normal distribution of data.

Levene’s test

Used to check if groups have comparable variance.

One-Sample t-test

Compares one sample mean to a reference value.

Wilcoxon Rank Test

A non-parametric alternative to the one-sample t-test.

Apophenia

The idea that we seek to find meaningful connections between unrelated things.

Pareidolia

The perception of images like faces in random stimuli.

Null hypothesis

The hypothesis of no effect or no difference.

Alternative hypothesis

The hypothesis that contradicts the null hypothesis, suggesting there is an effect or difference.

Type I error (false positive)

An error that occurs when a true null hypothesis is incorrectly rejected.

Type II error (false negative)

An error that occurs when a false null hypothesis is incorrectly not rejected.

One-tailed test

A test that specifies a direction for the hypothesis (greater than or less than).

Two-tailed test

A test that accounts for both possible differences (greater than or less than).

AI Hyperrealism

AI generated faces that human participants identify as human more often than real human faces.

T-value equation

The equation is t = RAC{\bar{X} - \mu}{(s / \sqrt{n})}, where \bar{X} is the sample mean, \mu is the population mean, s is the sample standard deviation, and n is the sample size. It measures how far the sample mean is from the population mean in terms of standard errors.

Degrees of freedom equation

Calculated as n - 1, where n is the number of observations in the sample. It indicates the number of independent pieces of information used to estimate a parameter.

Significance level

Often denoted as \,\alpha$, it defines the threshold for determining statistical significance. Common levels are 0.05 (5%) and 0.01 (1%).