The Hypothesis Testing Framework

Flashcards covering key concepts in the hypothesis testing framework and statistical inference.

Statistical inference

A process that converts data into useful information through questioning, collecting, summarizing, analyzing data, and interpreting results.

Population

The group that we want to learn about through our data.

Sample

A subset of the population used to make inferences about the entire population.

Null hypothesis (H0)

The hypothesis stating that there is no effect or relationship; 'nothing unusual is happening'.

Alternative hypothesis (H1 or HA)

The hypothesis stating that there is an effect or relationship; 'something is going on'.

p-value

The probability of observing data as extreme as the test statistic, assuming the null hypothesis is true.

Statistical significance

When the p-value is less than the significance level (α), indicating strong evidence against the null hypothesis.

Type I Error

Rejecting the null hypothesis when it is actually true.

Type II Error

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

Two-sided test

A hypothesis test that considers both directions of the effect (greater than and less than).

Significance level (α)

The threshold probability for rejecting the null hypothesis, commonly set at 0.05.