Statistical Significance Testing

These flashcards cover key terms and concepts related to statistical significance testing, including definitions of critical terms and explanations of key concepts.

Confidence Intervals

Communicates precision by providing a range of plausible values for the population parameter being estimated.

Effect Sizes

Communicates strength by telling us the magnitude of the experimental effect or relationship between variables.

Statistical Significance Testing

Communicates probability, telling us how likely the current result would be if the study’s null hypothesis were true.

Random Sampling Error

The natural deviations that occur when randomly sampling from the population.

Bias

Flawed sampling procedures where the researcher does not use a representative sample.

Sampling Distribution

The distribution of a sample statistic that would be obtained if all possible samples of the same size were drawn from a given population.

Central Limit Theorem

States that as the sample size increases, the sampling distribution tends to be normal regardless of the original population distribution.

Type I Error (Alpha)

When we reject the null hypothesis when it is true.

Type II Error (Beta)

When we fail to reject a null hypothesis when it is false.

Power (1-β)

The ability of the test to correctly reject a null hypothesis when it is false.

P-value

The probability that we would have the result found in our treatment group if the null hypothesis were true.

Statistical Significance

A result is statistically significant if a p-value is less than or equal to alpha (typically ≤ 0.05).