Data Analysis Flashcards

A study reports a relative risk (RR) of 2.0 with a 95% CI of 1.2–3.5. What can be concluded?

Answer: The exposed group has twice the risk, and the result is statistically significant

RR = 2.0 means twice the risk in the exposed group; since the 95% CI does not include 1.0, the result is statistically significant.

When calculating a non-parametric reference interval using the CLSI C28-A3 method with 120 samples, the 2.5th percentile is determined by:

Answer: Ranking data and selecting the 3rd lowest value

CLSI C28-A3 ranks the 120 values; the 2.5th percentile is the 3rd lowest value (rank 3 = 0.025 × 120).

Which of the following best describes a Type II error in hypothesis testing?

Answer: Failing to reject a false null hypothesis

A Type II error (false negative) occurs when a false null hypothesis is not rejected; its probability equals beta (β).

In a linear calibration curve, R² = 0.998 indicates:

Answer: 99.8% of the variation in response is explained by the analyte concentration

R² (coefficient of determination) represents the proportion of variance in the dependent variable explained by the independent variable.

Which statistical approach is used to adjust for multiple comparisons to control the false discovery rate?

Answer: Benjamini-Hochberg procedure

The Benjamini-Hochberg procedure controls the false discovery rate (FDR) and is less conservative than Bonferroni correction.

When external quality assurance (EQA) results show a consistent positive bias compared to peer group means, the most likely cause is:

Answer: A systematic calibration error

Consistent positive bias relative to peers indicates a systematic error, most commonly due to calibration offset or reagent lot differences.

In a scatter plot showing two variables, a Pearson r = −0.85 indicates:

Answer: A strong negative linear relationship

Pearson r = −0.85 indicates a strong negative linear relationship, with higher values of X consistently associated with lower values of Y.