The p-value is a statistical measure used in hypothesis testing to determine the probability of observing the data (or more extreme results) assuming the null hypothesis is true. It is commonly referred to as the level of significance, indicating whether the results are statistically significant when compared to a predetermined threshold (e.g., α = 0.05).
Option A (An index of data reliability): Reliability refers to the consistency of a measurement tool, not the function of a p-value. The p-value assesses the likelihood of results occurring by chance, not the reliability of the data collection process.
Option B (A level of significance): This is the correct answer. According to NAHQ CPHQ study materials, the p-value is used to determine statistical significance, helping quality professionals evaluate whether observed differences or outcomes (e.g., in quality improvement interventions) are likely due to chance. A low p-value (e.g., <0.05) suggests the results are significant, leading to rejection of the null hypothesis.
Option C (A measure of central tendency): Measures of central tendency (mean, median, mode) describe the center of a data distribution and are unrelated to the p-value, which is a hypothesis testing metric.
Option D (A degree of deviation): Deviation refers to variability measures like standard deviation, which quantify data spread. The p-value does not measure deviation but rather the significance of observed results.
[Reference: NAHQ CPHQ Study Guide, Domain 2: Health Data Analytics, covers statistical concepts, including the p-value as a measure of statistical significance critical for interpreting quality improvement data., , , , ]
