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Replication Power Calculator
Estimate the sample size and detectable effect needed for a replication study under conservative assumptions.
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Replication Power Calculator
Required sample size per group
150
Using a conservatively adjusted effect size of 0.375 (75% of the original 0.50).
- Original effect size (Cohen's d)
- 0.50
- Conservative adjusted effect
- 0.375
- Required n per group
- 150
- Total sample (2 groups)
- 300
Result chart
Formula
n per group ≈ 2 × [(z-alpha + z-beta) ÷ effect size]², the standard approximate sample size formula for a two-sample t-test. Original published effect sizes are commonly inflated (publication bias tends to favor larger, more "significant" effects), so applying a conservatism factor (deliberately assuming the true effect may be smaller than published) is standard, recommended practice for replication study planning.
Worked example
An original effect size of 0.5, conservatively adjusted to 0.375 (75%), targeting 90% power: requires roughly 149 participants per group - notably more than the original study likely used if it reported the unadjusted 0.5 effect.
Money-page insight
Replication studies routinely need LARGER samples than the original study, precisely because published effect sizes tend to be inflated (a well-documented phenomenon sometimes called "the winner's curse" in the replication literature) - planning around the original's effect size directly, without conservative adjustment, is a common reason replication attempts turn out underpowered.
Calculation history
Stored locally on this deviceHow the replication power calculator works
How to use this calculator
Adjust the assumptions on the left and the result updates instantly. Use the summary as a planning estimate, then compare it with quotes, local rules, lender disclosures, or professional guidance for decisions involving taxes, loans, construction, or health.
Useful next steps
Learn more
Stage 1 - Inputs
Collect the required replication study power planning values and confirm that each value is physically and logically possible.
Stage 2 - Formula
n per group ≈ 2 × [(z-alpha + z-beta) ÷ effect size]², the standard approximate sample size formula for a two-sample t-test. Original published effect sizes are commonly inflated (publication bias tends to favor larger, more "significant" effects), so applying a conservatism factor (deliberately assuming the true effect may be smaller than published) is standard, recommended practice for replication study planning.
Stage 3 - Substitute values
Replace each variable in the formula with the current input value. This keeps the calculation transparent and easy to audit.
Stage 4 - Intermediate calculations
Calculate the supporting values first, such as totals, rates, balances, volumes, or ratios, before producing the final result.
Common mistakes
- Mixing units, such as monthly and annual rates, inches and feet, or gross and net income
- Entering rounded guesses when exact quotes or measurements are available
- Ignoring fees, taxes, risk factors, local rules, or physical constraints
- Treating an estimate as a final professional decision
Tips
- Change one input at a time to understand sensitivity
- Use conservative assumptions when the result affects safety, debt, taxes, or health
- Save or print the result with assumptions before comparing alternatives
- Recheck units whenever a result looks surprisingly large or small
Replication Power Calculator mastery
Estimate the sample size and detectable effect needed for a replication study under conservative assumptions.
Use this statistics calculator as a working model: enter realistic inputs, read the primary answer first, then use the supporting rows to understand what changed and why.
Read the result correctly
Treat the primary answer as the headline result and the supporting values as the evidence trail behind it.
Improve accuracy
Small input changes can shift the output. Recheck units, time periods, percentages, and any assumptions before using the result.
Use it professionally
Save or print the result with the inputs visible so the calculation can be reviewed, repeated, or compared later.
Expert suggestions
Professional perspective
How to get more value from the replication power calculator
A strong calculation is not only a final number. It is a repeatable way to compare choices, understand assumptions, and see which inputs deserve the most attention.
Start with a baseline
Use the most realistic inputs you have today before testing optimistic or conservative cases.
Change one variable
Adjust one assumption at a time. This makes cause and effect easier to understand.
Keep the evidence visible
Save or export the result with inputs included so the answer can be checked later.
Learning path
What to understand next
- Understand the main formula
- Review the assumptions
- Compare alternate scenarios
- Decide what information would improve accuracy
Statistics insight guide
Understand the answer
Use the replication power calculator as a decision aid, not just a number.
A calculator is most useful when the result, assumptions, and practical meaning are read together. Use the output as a structured estimate and review the inputs before making a decision.
The primary answer summarizes the model. Supporting values explain the path from inputs to output and reveal which assumptions matter most.
The result usually changes when units, rates, time periods, quantities, prices, thresholds, or rounding assumptions change.
Confirm that each input uses the intended unit, time period, percentage basis, and sign. A correct formula can still produce a poor estimate from inconsistent inputs.
Use extra care when the answer affects money, health, safety, legal exposure, construction quantities, or long-term planning.
Accuracy checklist
- Confirm every unit before comparing outputs.
- Use current inputs rather than outdated estimates.
- Test at least one conservative and one optimistic scenario.
- Review whether rounding changes the practical decision.
How professionals use this
- Document the inputs beside the result.
- Compare scenarios instead of relying on a single run.
- Share the assumptions when asking for review.
- Use expert review for high-stakes decisions.
Frequently asked questions
- Why assume the true effect might be smaller than published?
- Publication bias means studies with larger, "significant" effects are more likely to get published - the true population effect is often smaller than what any single published study reports, which is why conservative adjustment is standard practice in replication planning.
- What conservatism factor should I use?
- There's no universal answer, but factors of 0.5-0.75 (assuming the true effect is 50-75% of the published one) are commonly used defaults in replication planning literature - the more skeptical you are of the original finding, the more conservative (lower) factor to apply.
- Is this the exact formula used by statistical software?
- This is a standard, widely-used approximation for two-sample comparisons - dedicated power analysis software (like G*Power) can provide more precise calculations for specific test types and more complex designs.
- What does the Replication Power Calculator calculate?
- Estimate the sample size and detectable effect needed for a replication study under conservative assumptions.
- How should I read the Replication Power Calculator result?
- Read the primary answer first, then review the supporting values, formula notes, assumptions, and expert suggestions. The supporting values explain why the answer moved and which inputs deserve more attention.
- Which input matters most in the Replication Power Calculator?
- The most important input depends on the calculator, but the highest-impact variables are usually rates, time periods, quantities, income, balance, measurements, or unit choices. Change one input at a time to see which variable drives the result.
- Why might my Replication Power Calculator result differ from another website?
- Different calculators may use different assumptions, rounding rules, formulas, default values, tax years, unit conversions, or included costs. Compare the formula and assumptions before comparing final answers.
- Can I use this statistics result for an important decision?
- Use the result as a structured estimate and learning tool. For financial, tax, medical, legal, construction, or safety-sensitive decisions, verify the inputs and review the output with a qualified professional.
- How often should I update the inputs in the Replication Power Calculator?
- Update the inputs whenever the underlying facts change: rates, prices, measurements, dates, balances, income, rules, or goals. Outdated inputs create outdated answers.
- What is the safest way to compare scenarios?
- Keep all inputs the same except one variable. That makes it clear whether the difference came from rate, time, quantity, price, measurement, or another assumption.