Statistical Measure for Disease Case Analysis
Use the median to determine the typical number of disease cases over different time periods when analyzing 14 case numbers. 1
Rationale for Median Selection
The median (option C) is the most appropriate measure for epidemiological case count data because it represents the central tendency without being influenced by extreme values or outliers that commonly occur in disease surveillance data. 1
Why Median is Superior
- The median represents the 50th percentile of the distribution and provides a robust measure of central tendency that is resistant to skewed data patterns typical in disease case reporting. 1
- Disease case counts frequently follow non-normal distributions with potential outliers (unusually high or low case numbers during specific periods), making the median more reliable than the mean. 1
- The American Thoracic Society specifically recommends using median values when expressing epidemiological data for small sample sizes, as it provides the most accurate representation of typical variation. 1
Why Other Options Are Inappropriate
- Mean (option A) would be heavily influenced by extreme values - if one time period had an unusually high outbreak, it would artificially inflate the "typical" case count, misrepresenting the usual disease burden 1
- Mode (option B) only identifies the most frequently occurring value - with 14 different numbers, there may be no repeating values, making this measure uninformative for understanding typical case loads 1
- Range (option D) describes the spread between minimum and maximum values - this tells you about variability but provides no information about the typical or central number of cases occurring during most time periods 1
Epidemiological Context
Frequency measures like incidence are fundamental in descriptive epidemiology for studying how disease is distributed in populations across time. 2 When an epidemiologist collects case counts over different time periods, the goal is to understand the typical disease burden and temporal patterns, which requires a measure of central tendency rather than dispersion or frequency alone. 2