Chapter 1–2 Notes: Probability, p-values, and Errors in Hypothesis Testing

Flashcards covering key concepts from the video notes on hypothesis testing, p-values, alpha, and common errors.

Alpha value (significance level)

The probability threshold for declaring a result statistically significant, commonly 0.05.

p-value

The probability, assuming the null hypothesis is true, of obtaining results as extreme or more extreme than observed.

Extreme (p-value context)

Results that are far from what is expected under the null hypothesis; correspond to low probability under random chance.

Null hypothesis

The default assumption tested, usually that there is no real difference or effect.

Alternative hypothesis

The claim that there is a real difference or effect (the hypothesis you may accept if you reject the null).

Type I error

Rejecting a true null hypothesis; a false positive.

Type II error

Failing to reject a false null hypothesis; a false negative.

Bias

Systematic error in data collection that can skew results.

Outliers

Data points that lie far from the rest of the data and can distort results.

Random chance

Natural variability; p-values assess how likely results could occur by chance under the null.

Statistical significance

A result is statistically significant if the p-value is at or below the alpha level.