Exam 2: Inferential Statistics Flashcards
Inferential Statistics
A branch of statistics that allows researchers to make inferences or generalizations about a population based on data from a sample.
Population
The entire group a researcher is interested in studying or drawing conclusions about.
Sample
A subset of the population that is used to represent the entire group in a study.
Null Hypothesis (H₀)
A statement that there is no effect or no difference, used as the default assumption in hypothesis testing.
Alternative Hypothesis (H₁)
The hypothesis that there is an effect or difference, tested against the null hypothesis in inferential statistics.
p-value
The probability of observing a result as extreme as, or more extreme than, the one observed, assuming that the null hypothesis is true.
Statistical Significance
A result is statistically significant if the p-value is below a predetermined threshold (typically 0.05), suggesting the null hypothesis can be rejected.
Type I Error
Incorrectly rejecting the null hypothesis when it is true (false positive).
Type II Error
Failing to reject the null hypothesis when it is false (false negative).
t-test
A statistical test used to compare the means of two groups to determine if they are significantly different from each other.
ANOVA (Analysis of Variance)
A statistical method used to compare the means of three or more groups to see if they are significantly different.
Effect Size
A measure of the strength or magnitude of an effect, independent of sample size.
Sampling Distribution
The probability distribution of a given statistic based on a random sample.
Publication Bias (File Drawer Effect)
The tendency for studies with significant results to be published more frequently than those with null or non-significant results.
p-hacking
The practice of manipulating data analysis to obtain statistically significant p-values, often through selective reporting or running multiple analyses.
Replication Crisis
A methodological issue where many scientific studies, especially in psychology, fail to be replicated, raising concerns about the reliability of findings.
Pre-Registration
The practice of publicly registering the study’s design, hypotheses, and analysis plan before conducting the research to reduce p-hacking and improve transparency.
Open Science
An approach to research that promotes sharing data, methods, and findings openly to improve transparency and reproducibility in science.
The Decision Rule
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).
Hypothesis Testing
A statistical process to determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.
