Health Data Analytics: Mastering Statistical Methods

Health Data Analytics is the second-largest domain on the CPHQ exam. While you will not perform complex calculations, you must understand which statistical methods to use and how to interpret results.

Data Types Matter

The type of data determines which statistical test to use. Nominal data is categorical with no order (gender, diagnosis type). Ordinal data has categories with a natural order (satisfaction ratings, pain scales). Interval data has equal spacing between values but no true zero (temperature in Celsius). Ratio data has equal spacing and a true zero (age, weight, length of stay).

Descriptive Statistics

Know the measures of central tendency (mean, median, mode) and when each is most appropriate. The mean is affected by outliers; the median is resistant to outliers and better for skewed data. Know standard deviation as a measure of spread and how it relates to the normal distribution (68-95-99.7 rule).

Common Statistical Tests

Chi-square: Tests relationships between categorical variables. t-test: Compares means between two groups. ANOVA: Compares means among three or more groups. Correlation: Measures the strength and direction of a linear relationship. You do not need to calculate these, but you need to select the right test for a given scenario.

Validity and Reliability

Validity asks "does it measure what it claims to measure?" Reliability asks "does it produce consistent results?" A measure can be reliable without being valid, but cannot be valid without being reliable.