Chapter 13_Inferential Statistics

Question 1

Inferential Statistics

Answer 1

A branch of statistics that allows researchers to make inferences or generalizations about a population based on data from a sample.

Question 2

Population

Answer 2

The entire group a researcher is interested in studying or drawing conclusions about.

Question 3

Sample

Answer 3

A subset of the population that is used to represent the entire group in a study.

Question 4

Null Hypothesis (H₀)

Answer 4

A statement that there is no effect or no difference, used as the default assumption in hypothesis testing.

Question 5

Alternative Hypothesis (H₁)

Answer 5

The hypothesis that there is an effect or difference, tested against the null hypothesis in inferential statistics.

Question 6

p-value

Answer 6

The probability of observing a result as extreme as, or more extreme than, the one observed, assuming that the null hypothesis is true.

Question 7

Statistical Significance

Answer 7

A result is statistically significant if the p-value is below a predetermined threshold (typically 0.05), suggesting the null hypothesis can be rejected.

Question 8

Type I Error

Answer 8

Incorrectly rejecting the null hypothesis when it is true (false positive).

Question 9

Type II Error

Answer 9

Failing to reject the null hypothesis when it is false (false negative).

Question 10

t-test

Answer 10

A statistical test used to compare the means of two groups to determine if they are significantly different from each other.

Question 11

ANOVA (Analysis of Variance)

Answer 11

A statistical method used to compare the means of three or more groups to see if they are significantly different.

Question 12

Effect Size

Answer 12

A measure of the strength or magnitude of an effect, independent of sample size.

Question 13

Sampling Distribution

Answer 13

The probability distribution of a given statistic based on a random sample.

Question 14

Publication Bias (File Drawer Effect)

Answer 14

The tendency for studies with significant results to be published more frequently than those with null or non-significant results.

Question 15

p-hacking

Answer 15

The practice of manipulating data analysis to obtain statistically significant p-values, often through selective reporting or running multiple analyses.

Question 16

Replication Crisis

Answer 16

A methodological issue where many scientific studies, especially in psychology, fail to be replicated, raising concerns about the reliability of findings.

Question 17

Pre-Registration

Answer 17

The practice of publicly registering the study’s design, hypotheses, and analysis plan before conducting the research to reduce p-hacking and improve transparency.

Question 18

Open Science

Answer 18

An approach to research that promotes sharing data, methods, and findings openly to improve transparency and reproducibility in science.

Question 19

The Decision Rule

Answer 19

A guideline in hypothesis testing for deciding whether to reject the null hypothesis, based on the p-value and a pre-set significance level (e.g., 0.05).

Question 20

Hypothesis Testing

Answer 20

A statistical process to determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.