One Sample Mean Test Power Calculator
Estimates approximate two-sided power for a one-sample mean test. The form displays approximate normal power beside one sample mean test power, using a worked condition that can be recalculated with the labeled inputs.
Set the labeled inputs
One Sample Mean Test Power
Scope of the one sample mean test power method
The one sample mean test power page estimates approximate two-sided power for a one-sample mean test.
One Sample Mean Test Power is limited to the statistical quantity named by the result panel. The one sample mean test power calculation does not silently add a population, time horizon, causal direction, or decision threshold that is absent from the fields.
Reading the one sample mean test power fields
- Expected mean: For one sample mean test power, the worked value for expected mean is 12 units. Treat the expected mean entry (12 units) explicitly as a count, proportion, rate, estimate, or model parameter before comparing one sample mean test power conditions.
- Null mean: For one sample mean test power, the worked value for null mean is 10 units. Treat the null mean entry (10 units) explicitly as a count, proportion, rate, estimate, or model parameter before comparing one sample mean test power conditions.
- Standard deviation: For one sample mean test power, the worked value for standard deviation is 4 units. Treat the standard deviation entry (4 units) explicitly as a count, proportion, rate, estimate, or model parameter before comparing one sample mean test power conditions. The form enforces minimum 1e-06.
- Sample size: For one sample mean test power, the worked value for sample size is 30 observations. Treat the sample size entry (30 observations) explicitly as a count, proportion, rate, estimate, or model parameter before comparing one sample mean test power conditions. The form enforces minimum 2.
The entries used for one sample mean test power must refer to one coherent analysis condition. Combining incompatible populations, periods, or measurement definitions can produce valid one sample mean test power arithmetic for a nonexistent study.
The arithmetic used for one sample mean test power
For one sample mean test power, match every symbol in the relationship to a labeled field before substituting numbers. One Sample Mean Test Power is reported in the scale implied by the inputs and formula.
While checking one sample mean test power, change Expected mean by a small controlled amount and predict the direction of one sample mean test power before recalculating.
The surrounding workflow may also require two sample mean test power.
Worked values for one sample mean test power
The default one sample mean test power condition is Expected mean = 12 units, Null mean = 10 units, Standard deviation = 4 units, Sample size = 30 observations.
A mean difference of 2 with SD 4 and n=30 gives approximate two-sided normal power of 0.7819.
The live calculator reports Approximate power 0.78189745. Repeating one intermediate step from approximate normal power provides a fixed one sample mean test power reference check for later code changes.
Statistical context for one sample mean test power
This normal approximation assumes a planned standard deviation, effect, alpha of .05, and independent observations.
For one sample mean test power, power and design calculations are prospective scenarios, not guarantees. For one sample mean test power, their answer changes when the planned effect, variance, allocation, alpha, or attrition assumption changes.
How to interpret the one sample mean test power output
When interpreting one sample mean test power, report the design inputs as assumptions and compare at least one plausible alternative before committing resources to the plan.
As a second check for one sample mean test power, if that direction is surprising, recheck the units and the role of Sample size in approximate normal power before accepting the display.
Varying a single one sample mean test power input at a time
Change expected mean while holding the remaining entries fixed, then state why the direction and size of the one sample mean test power change are plausible from approximate normal power.
Repeat the one sample mean test power exercise with sample size. If a modest defensible change materially alters the interpretation, report both conditions rather than presenting that one sample mean test power scenario as exact.
Input and rounding traps
Before accepting one sample mean test power, compare every entered value with its label, unit, and allowed domain after reading the printed relationship from left to right.
For one sample mean test power, do not move a number between fields merely because the units look compatible; each label gives the number a different statistical role.
Another one sample mean test power failure occurs when a rounded output is reused as though it were the original measurement. Carry guard digits through calculations that depend on one sample mean test power, then round only the reported value.
What to record with one sample mean test power
Report one sample mean test power using approximate normal power, followed by the entered values, units, exclusions, and analysis date. Name the one sample mean test power population or dataset boundary instead of leaving it implicit.
Keep the full calculator output with the record, including Approximate power 0.78189745. A later one sample mean test power review can then distinguish a changed input from a different convention or software implementation.
Questions about one sample mean test power
Why could another program report a different one sample mean test power?
A different convention for rounding, tails, ties, interpolation, parameterization, or missing values can change one sample mean test power. Compare the printed one sample mean test power formula and its input definitions before treating either output as wrong.
What does one sample mean test power represent on this page?
It is the quantity produced by approximate normal power from the displayed expected mean, null mean, standard deviation, sample size. This page estimates approximate two-sided power for a one-sample mean test.