Independent t-Test Power .80 Sample Size
Explicit equal allocation independent two-sample NORMAL approximation per group80percent power budget; not exact noncentraltpower or observed-effect guarantee. Actual independently qualified instrument/cohort/matching units; no diagnosis, causal outcome, validated personal prediction or universal clinical cutoff.
Description
Two-group power planning requires a proposed effect and significance level.
Two-group power planning requires a proposed effect and significance level.1
A normal approximation provides a conditional per-group budget rather than exact t-test power. The calculation and input requirements are explained below.
When to use Independent t-Test Power .80 Sample Size
Enter two-sided alpha strictly between 0 and 1 and a positive independently proposed standardized effect.
How to use Independent t-Test Power .80 Sample Size and how it works
- Prepare the measurements or independently established model inputs described below, with matching units and observation scope.
- Enter the required fields and run Independent t-Test Power .80 Sample Size.
- Read the result fields using the formula, output units, signs and ordering explained below.
- Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| callerTwoSidedAlpha | Caller Two Sided Alpha. Use the units and matching context stated in the calculation below. | number | Required; fraction from 0 to 1; finite; other constraints below |
| callerIndependentExpectedEffectSize | Caller Independent Expected Effect Size. Use the units and matching context stated in the calculation below. | number | Required; strictly positive; finite; other constraints below |
Enter two-sided alpha strictly between 0 and 1 and a positive independently proposed standardized effect. Use normal quantiles at 1-alpha/2 and 0.8, calculate twice their squared sum divided by effect squared and round upward. Return required participants per group under the equal-allocation normal approximation, not a total sample or exact noncentral-t solution.
perGroupN=ceil(2*(z_(1-alpha/2)+z_0.8)^2/effect^2) Apply the input ordering, units and branch conditions stated above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.value | Numeric result under the formula and output units explained above. | number |
{
"input": {
"callerTwoSidedAlpha": 0.05,
"callerIndependentExpectedEffectSize": 0.5
},
"output": {
"result": {
"value": 63
}
}
}Limitations and assumptions
The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use SEM Minimum Sample 10-Per-Parameter Rule for a complementary operation: A participants-per-parameter heuristic is a nominal planning budget.
Use Post-Hoc Power From d and n Calculator for a complementary operation: Observed post-hoc power is derived from the observed effect and test design.
References
-
Independent t-Test Power .80 Sample Size — tool.io
Similar or alternative tools
- SEM Minimum Sample 10-Per-Parameter Rule
A participants-per-parameter heuristic is a nominal planning budget.
- Post-Hoc Power From d and n Calculator
Observed post-hoc power is derived from the observed effect and test design.