Null and Alternative Hypothesis, Type I & II Errors and Test of Significance

Flashcards covering key concepts of Hypotheses and Errors in Statistical Testing.

Null Hypothesis

The hypothesis that states there is no effect or no difference; it is the opposite of what the experimenter believes.

Alternative Hypothesis

The hypothesis that states there is an effect or a difference; it represents what the researcher aims to support.

Type I Error

The error made when a true null hypothesis is incorrectly rejected.

Type II Error

The error made when a false null hypothesis fails to be rejected.

Statistical Significance

A determination that the observed effect in the study is unlikely to be due to chance, typically represented by a p-value.

p-value

The probability level that indicates the strength of the evidence against the null hypothesis.

One-tailed Test

A statistical test that examines whether a parameter is either greater or less than a certain value, but not both.

Two-tailed Test

A statistical test that examines whether a parameter is significantly different from a certain value in either direction.

Inferential Statistics

Statistics used to make inferences or generalizations about a population based on a sample.

Sampling Error

The error that occurs when a sample does not accurately reflect the population from which it was drawn.