Introduction to Statistical Inference and Hypothesis Testing

These flashcards cover key terms and concepts related to statistical inference and hypothesis testing, useful for understanding the foundational principles taught in the lecture.

Descriptive Statistics

Procedures that summarize the characteristics of a set of data, such as mean, median, standard deviation, and interquartile range.

Inferential Statistics

Procedures that allow us to draw conclusions about a population based on a sample, estimating how representative the sample data is of the population.

Sampling Error

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

Null Hypothesis (H0)

A statement asserting that there is no effect or no difference, often representing the current accepted position.

Alternative Hypothesis (H1)

A statement asserting that there is an effect or a difference, representing the researcher's prediction.

Alpha Level

The threshold probability (e.g., 0.05 or 0.01) for rejecting the null hypothesis, indicating the risk of a Type I error.

One-Tailed Hypothesis

A hypothesis that specifies a direction of the expected difference (e.g., H1: Drug users will score significantly less than non-drug users).

Two-Tailed Hypothesis

A hypothesis that does not specify the direction of the expected difference (e.g., H1: There is a statistically significant difference between drug users and non-drug users).

Cutoff Score

The threshold score on the distribution used to decide whether to reject the null hypothesis.

Statistical Significance

The result indicating that the observed data is unlikely to have occurred under the null hypothesis, typically evaluated at the alpha level.