Wk 10 (slides): Inferential Statistics

These flashcards cover key terminology and concepts related to inferential statistics as presented in the lecture.

Inferential Statistics

A branch of statistics used to test whether differences or associations among data occurred by chance or represent true patterns.

Statistical Significance

Findings that are unlikely to occur due to natural chance or variability, indicating that the results are meaningful.

Normal Distribution

A bell-shaped curve displaying data plotted as frequencies, arising from random variation.

Parametric Tests

Statistical tests that assume underlying statistical distributions in the data, such as the t-test and ANOVA.

Non-Parametric Tests

Statistical tests that do not assume a specific distribution of the data, such as the Mann Whitney U Test and the Kruskal Wallis Test.

P-Value

The probability level used to determine statistical significance, indicating the likelihood of observing the results by chance.

Clinical Significance

A health outcome change identified as important by practitioners, highlighting the need to pay attention to such changes.

Correlation

A mutual relationship that describes the degree of relationship between two variables, ranging from -1 to 1.

Regression

A statistical method used to examine the relationship between one dependent variable and one or more independent variables.

Survival Analysis

A technique used to study the length of time until the occurrence of an event, utilizing methods such as Kaplan Meier curves.

Generalizability

The applicability of research findings to real-world settings or populations, emphasizing external validity.

Benchmarking

An evaluation approach that compares interventions or outcomes against established standards to promote best practices.

Before/After Comparison

A method to assess changes over time in paired samples, often using t-tests or Wilcoxon signed-rank tests to assess significance.

Assumptions for Parametric Tests

Conditions that must be met for parametric tests, including equal variances, interval or ratio scale data, and normal distribution.

Confounders

Other factors that may influence the results of a study and need to be controlled for when interpreting outcomes.