Making Sense of Inferential Statistics

These flashcards are designed to cover key concepts and terms related to inferential statistics, significant results of studies, and various statistical methods.

Statistical Significance

A term used to determine if the results of a study are likely to be genuine and not due to chance.

Margin of Error

The range that indicates how much the poll results could differ from the true population value, often expressed as a percentage.

Null Hypothesis

A statement in statistics that indicates no effect or no difference; for example, there is no significant difference between groups.

Sampling Distribution

The distribution of sample statistics over multiple samples, used to understand variability and infer population parameters.

Standard Error (SE)

A measure of the variability of a sample statistic from the population parameter, indicating the precision of the estimate.

Confidence Interval

A range of values derived from sample data that is likely to contain the true population parameter, given a specified confidence level.

Statistical Power

The probability that a statistical test will correctly reject a false null hypothesis, indicating the sensitivity of the test.

Practical Significance

The real-world relevance of a finding; even if a result is statistically significant, it may not be of practical importance.

Hypothesis Testing

A method for testing a hypothesis by comparing the p-value against a predetermined significance level, usually 0.05.

Type I Error

The mistake of rejecting a true null hypothesis, leading to the conclusion that a difference exists when it does not.

Type II Error

The failure to reject a false null hypothesis, leading to the conclusion that there is no difference when one actually exists.

Empirical Rule

A statistical rule stating that for a normal distribution, approximately 68% of values fall within one standard deviation of the mean.

Regression Analysis

A statistical process for estimating the relationships among variables, often used to determine the strength of predictors.

Dummy Variable

A binary variable used in regression analysis to represent categorical data with two levels (e.g., male/female).

Multicollinearity

A statistical phenomenon in which two or more independent variables in a regression model are highly correlated, affecting coefficient estimates.

Nominal Variable

A categorical variable with no intrinsic ordering, such as gender or race.

Ordinal Variable

A categorical variable with a clear ordering, such as satisfaction ratings from very dissatisfied to very satisfied.

Logistic Regression

A statistical method for predicting binary outcomes based on one or more predictor variables.

Factor Analysis

A statistical method used to identify underlying relationships between variables by grouping them into factors.

Survival Analysis

A set of statistical methods for analyzing time-to-event data, often used in medical research.