The Three E’s of Quantitative Research: Estimand, Estimator, Estimate
- Mayta
- Jun 19
- 1 min read
The Three E’s of Quantitative Research
Term | One-line definition | Where it “lives” | Simple example (blood-pressure drug) |
Estimand | The scientific question expressed as a precise quantity you want to know. | Before data are collected. | “The average reduction in systolic BP (mm Hg) if every adult took Drug X instead of placebo for 12 weeks.” |
Estimator | The recipe or formula you will apply to data to target the estimand. | In your analysis plan / code. | “Difference in sample means between the Drug X group and placebo group (unadjusted).” |
Estimate | The numerical answer you get after running the estimator on the actual dataset. | In your results table. | “–7.4 mm Hg (95 % CI –9.1 to –5.7).” |
How they fit together
Start with the estimand.
Clarifies what effect, in whom, and under which conditions you care about.
Choose an estimator that is valid if the required causal/statistical assumptions hold.
Could be as simple as a risk difference, or as complex as a targeted-maximum-likelihood algorithm.
Compute the estimate once you have data.
This single number (plus its uncertainty) is what you report.
Key points to remember
Estimand ≠ Estimator: The former is a target; the latter is a tool.
Different estimators (e.g., regression with covariate adjustment, IPW, G-computation) can aim at the same estimand.
The estimate is data-dependent and will change with a new sample; the estimand and estimator do not.
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