Inferential Statistics & Hypothesis Testing

These flashcards cover essential concepts related to inferential statistics and hypothesis testing, including types of hypotheses, errors, statistical tests, and definitions important for understanding the material.

Inferential Statistics

Using a sample to make conclusions about a population.

Null Hypothesis (H₀)

Predicts no relationship or no difference between variables.

alternative hypothesis

Predicts that there is a relationship or difference between variables.

Significance Level (α)

The standard threshold for determining statistical significance, commonly set at 0.05.

Type I Error

Rejecting a true null hypothesis, also known as a false positive.

Type II Error

Failing to reject a false null hypothesis, also known as a false negative.

Chi-Square Test

A statistical test used to determine if there is a significant association between categorical variables.

Contingency Table

A table used to display the frequency distribution of variables.

Observed Frequencies

The actual counts recorded in a contingency table.

Expected Frequencies

The counts expected if variables were independent, calculated using the formula E=(row total)(column total)/grand total.

Critical Value

A value from a statistical table used to determine whether to reject the null hypothesis.

Degrees of Freedom (df)

Calculated as (rows - 1)(columns - 1), used in various statistical tests.

Statistical Independence

A situation where two variables are not related to each other.

Statistical Dependence

A situation where two variables are related to each other.

p-value

The probability that the observed results occurred by chance; significant if p < 0.05.

Independent Samples t-Test

Compares the means of two separate groups.

Dependent Samples t-Test

Compares means from the same individuals measured at different times.

Pooled Variance

Assumes equal variances between groups in independent t-tests.

Separate Variances

Used when groups have unequal variances in independent t-tests.

One-Tailed Test

Predicts the direction of the difference between groups.

Two-Tailed Test

Predicts a difference between groups, but not the direction.