Statistical Methods and Study Design Quiz

Mean is a measure of central tendency representing the average value of a dataset.

Convenience sampling involves selecting subjects based on their easy accessibility and willingness to participate.

The Chi-square test assesses the association between two categorical variables in a contingency table.

A positive correlation coefficient suggests that as one variable increases, the other variable tends to increase as well.

P-value indicates the likelihood of obtaining the observed data if the null hypothesis were true.

Randomization ensures that treatment groups are comparable, reducing bias and confounding factors.

Blinding prevents bias by keeping participants and/or researchers unaware of the treatment assignments.

Case-control studies analyze associations between diseases and potential risk factors through retrospective observation.

Pilot studies collect initial data and evaluate the practicality and viability of conducting a larger study.

Stratified random sampling ensures representation of subgroups within a population, helping control for confounding variables.

Probability sampling ensures each member of the population has a known chance of being selected, while non-probability sampling lacks this randomness.

The null hypothesis in a one-sample t-test asserts that there is no difference between the sample mean and a specified population mean.

Control groups provide a baseline for comparison, allowing researchers to isolate and measure the effects of the independent variable.

Cross-sectional studies offer insights into the prevalence of a condition or characteristics within a population at a single time point.

The Wilcoxon signed-rank test compares paired samples when assumptions of normality are not met.

Observational studies passively observe variables, while experimental studies actively intervene and control variables.

The independent samples t-test compares means of two groups drawn from independent populations.

Crossover studies assess the effects of multiple treatments within the same subjects over distinct time periods.

Non-parametric tests are suitable for data with extreme values, as they do not rely on assumptions about the data distribution.

The Kaplan-Meier estimator calculates the probability of surviving up to a given time point in survival analysis.

Sensitivity analysis examines how changes in model assumptions or parameters affect the results, assessing the model's robustness.

Factors in factorial design represent independent variables manipulated to observe their effects on the dependent variable.

Meta-analysis synthesizes findings from various studies to provide a comprehensive understanding of a specific research question.

Statistical power measures the probability of correctly rejecting the null hypothesis when it is indeed false.