Statistics Key Concepts

These flashcards cover key statistical terms and their definitions from the lecture notes.

Population

The entire group we want information about.

Sample

A smaller subset of the population from which data is collected.

Parameter (μ, p)

A numerical summary describing the population.

Sample statistic (x̄, p̂)

Numerical summary from the sample, used to estimate the parameter.

Sampling distribution

Distribution of sample statistics if we took many samples.

Standard error (SE)

The standard deviation of the sampling distribution; measures sampling variability.

Bootstrap sample

Resample with replacement from your sample data.

Bootstrap distribution

Distribution of bootstrap statistics used to find confidence intervals.

Randomization distribution

Distribution assuming the null hypothesis is true, used to find p-values.

Confidence interval (CI)

Range of plausible values for the parameter.

p-value

Probability of observing a sample as extreme (or more) if the null hypothesis is true.

Null hypothesis (H₀)

Claim of 'no effect' or 'no difference'.

Alternative hypothesis (Hₐ)

What you are trying to find evidence for.

Left-tail / right-tail / two-tail tests

Determines which side of the distribution you’re checking for extremeness.

z-score

Standardized value: how many standard errors away from the mean a sample is.