---
title: "Power and Effect Size, Decided Before the Data"
description: "Power depends on the effect size worth detecting, the variability and n, and it has to be settled beforehand rather than computed afterwards from what happened. This stop runs the calculation and trea"
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source: https://lightmysky.com/learn/science/power-and-effect-size-decided-before-the-data-mt_zYNmSWAJT8.md
retrieved: 2026-09-12
---

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# Power and Effect Size, Decided Before the Data

Power depends on the effect size worth detecting, the variability and n, and it has to be settled beforehand rather than computed afterwards from what happened. This stop runs the calculation and treats the smallest useful effect as a scientific decision.

Subject: Science · Area: Scientific Inquiry · Ages 22 to 24
Page: https://lightmysky.com/learn/science/power-and-effect-size-decided-before-the-data-mt_zYNmSWAJT8

## Ready when they can

- Compute the sample size needed for a stated effect size, variability and power
- Explain why power computed after the fact from the observed effect adds nothing
- Argue why a significant result from an underpowered study tends to overstate the effect

## Lesson: Planning power before you collect data

You fix power before data collection, not after. Power is your chance of catching a real effect, set by the effect size, the variability, the sample size, and the significance threshold. Small noisy studies start weak and stay weak no matter how results look afterward.

You trade the levers deliberately. Bigger effects, quieter measurement, larger samples, or looser thresholds each raise power, and each has a price. You state the smallest effect worth finding and convert it into the sample you must enroll.

**Example.** You finish a study and compute power from your observed effect. Your mentor stops you: the observed effect already decided the p value, so the calculation launders the same number into new clothes. Honest power always uses an effect chosen before seeing data.

**Tip.** You distrust significant hits from underpowered work. Only inflated estimates clear the bar when power is low, so published winners overstate the truth on average. You run sensitivity checks across plausible effects, and you replicate with proper power.

**Recap.** You choose the effect first, you size the study for it, and you never compute power from what happened.

## Practice

8 questions on this page, each with its working shown.

## Needs first

- [Pseudoreplication: When n Is Smaller Than It Looks](https://lightmysky.com/learn/science/pseudoreplication-when-n-is-smaller-than-it-looks-mt_2gRWB6RTaI)
- [Errors, Power and the Design of a Test](https://lightmysky.com/learn/mathematics/errors-power-and-the-design-of-a-test-mt_D7ZXbvSD0l)

## Opens up

- [Researcher Degrees of Freedom and the Pre-registered Analysis Plan](https://lightmysky.com/learn/science/researcher-degrees-of-freedom-and-the-pre-registered-analysis-plan-mt_UJgZQ2AADm)