Stats Topic 10 Flashcards
Inferential statistical tests
help determine whether observed relationships between variables in a sample are likely to exist in the broader population.
Correlation measures …
measures the strength and direction of a linear relationship between two variables.
Partial correlation refines…
refines this by isolating the direct relationship between two variables while controlling for a third.
Statistical significance (p-value) and confidence intervals (CIs)
provide evidence for whether an observed relationship is real or due to chance.
Type I and Type II errors
must be considered to ensure accuracy in hypothesis testing.
Partial Correlation
A method for measuring the correlation between two variables while controlling for a third variable.
- Helps separate direct and indirect influences in a relationship.
Confidence Intervals (CIs)
Provide a range of values within which the true population parameter (e.g., mean, correlation coefficient) is likely to fall.
- Helps assess the reliability and precision of statistical estimates.
Reporting Partial Correlation
Helps clarify how two variables are related after controlling for other confounding variables.
- Results should be reported in the format
Using Confidence Intervals
A confidence interval that does not include zero indicates a statistically significant correlation.
- Allows estimation of effect size and practical significance.
- Used in regression analysis to interpret the slope of relationships between predictors and outcomes.
Meta-Analysis
- Combines results from multiple studies to identify overall trends and effect sizes.
- Helps address inconsistencies and improve the reliability of conclusions.
Assessing Validity
- Studies should be evaluated for statistical power, sample size, and methodological rigor.
- Confidence intervals and effect sizes should be considered rather than relying solely on p-values.
Practical Implications
- Findings should be interpreted in context, considering potential confounding variables.
- Statistical relationships do not imply causation; experimental controls and longitudinal studies are needed for causal inference.
Type 1 Error
A Type I error occurs when a researcher incorrectly rejects a true null hypothesis.
Type 2 Error
A Type II error happens when a researcher fails to reject a false null hypothesis.
Spearman’s Correlation
- A non-parametric measure of correlation that assesses the strength and direction of a monotonic relationship between two ranked variables.
- Used for ordinal data or non-normally distributed data.
Pearson’s Correlation
- A parametric measure of correlation that assesses the strength and direction of a linear relationship between two continuous variables.
- Used for normally distributed data.
