Reliability, Validity, and Representativeness — Side by Side

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⚖️ Reliability, Validity, and Representativeness — Side by Side

Concept Key Question Example of a Problem Example of an Improvement
Reliability "If I repeated this measurement, would I get the same result?" Three students measure the same river width and get 3.2 m, 4.1 m, and 3.7 m — inconsistent technique Take three repeat measurements at each site and calculate the mean; standardise who holds the tape and where it is anchored
Validity "Does this data actually measure what I'm trying to measure?" Using the number of cars parked in a street as a measure of deprivation — car ownership is linked to income but is not a direct measure of deprivation Use multiple indicators of deprivation (e.g., house condition, garden maintenance, litter levels) and combine them into an index
Representativeness "Does my sample fairly represent the whole population or area?" Conducting a questionnaire only on Saturday lunchtime outside a shopping centre — this sample over-represents shoppers and weekend visitors Use stratified sampling across different times of day and days of the week; vary the location within the study area

Note: A dataset can be reliable but not valid (consistent measurements of the wrong thing), or valid but not reliable (correct measures that vary too much). The ideal is data that is both reliable AND valid AND representative.

Quick Check: Suggest two ways to improve the reliability of a river velocity investigation.

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