Kish Effective Sample Size Calculator
Estimates effective sample size from unequal positive analysis weights using Kish’s approximation. The form displays neff = (sum wi)^2 / sum(wi^2) beside kish effective sample size, using a worked condition that can be recalculated with the labeled inputs.
Add the estimate and precision values
Kish effective sample size
The question behind kish effective sample size
The kish effective sample size page estimates effective sample size from unequal positive analysis weights using Kish’s approximation.
Kish effective sample size is limited to the statistical quantity named by the result panel. The kish effective sample size calculation does not silently add a population, time horizon, causal direction, or decision threshold that is absent from the fields.
How the inputs shape kish effective sample size
- Survey weights: For kish effective sample size, the displayed survey weights sequence is 0.8, 0.9, 1.0, 1.0, 1.1, 1.2, 1.4, 1.6 weights. Preserve survey weights order when kish effective sample size depends on pairing, lag, rank, or time position, and distinguish an observed zero from a missing survey weights entry.
The entries used for kish effective sample size must refer to one coherent analysis condition. Combining incompatible populations, periods, or measurement definitions can produce valid kish effective sample size arithmetic for a nonexistent study.
How kish effective sample size is calculated
For kish effective sample size, match every symbol in the relationship to a labeled field before substituting numbers. Kish effective sample size is reported in observations.
While checking kish effective sample size, use survey weights observations from one defined analysis set rather than totals copied from incompatible groups.
Checking the displayed example
The default kish effective sample size condition is Survey weights = 0.8, 0.9, 1.0, 1.0, 1.1, 1.2, 1.4, 1.6 weights.
The eight example weights produce an effective sample size below the actual count of eight.
The live calculator reports Kish effective sample size 7.62711864 observations · Actual weight count 8 weights. Repeating one intermediate step from neff = (sum wi)^2 / sum(wi^2) provides a fixed kish effective sample size reference check for later code changes.
What the kish effective sample size arithmetic assumes
This approximation reflects weight variability but not clustering, stratification, or estimator-specific design effects.
For kish effective sample size, a planning or survey quantity is only as defensible as its frame, response assumptions, clustering, and population definition.
When kish effective sample size can mislead
When interpreting kish effective sample size, changing a design effect, allocation rule, response rate, or finite-population boundary can matter more than another displayed decimal.
As a second check for kish effective sample size, outliers, ties, ordering, and missing entries can affect kish effective sample size even when the number of observations stays unchanged.
Testing how stable kish effective sample size is
Change survey weights while holding the remaining entries fixed, then state why the direction and size of the kish effective sample size change are plausible from neff = (sum wi)^2 / sum(wi^2).
Repeat the kish effective sample size exercise with survey weights. If a modest defensible change materially alters the interpretation, report both conditions rather than presenting that kish effective sample size scenario as exact.
Rebuilding this kish effective sample size calculation later
Report kish effective sample size using neff = (sum wi)^2 / sum(wi^2), followed by the entered values, units, exclusions, and analysis date. Name the kish effective sample size population or dataset boundary instead of leaving it implicit.
Keep the full calculator output with the record, including Kish effective sample size 7.62711864 observations · Actual weight count 8 weights. A later kish effective sample size review can then distinguish a changed input from a different convention or software implementation.
The surrounding workflow may also require finite population correction.
Questions about kish effective sample size
What does kish effective sample size represent on this page?
It is the quantity produced by neff = (sum wi)^2 / sum(wi^2) from the displayed survey weights. This page estimates effective sample size from unequal positive analysis weights using Kish’s approximation.
What should be saved with kish effective sample size?
Save the entered values and units for survey weights, along with the analysis date, exclusions, software or formula version, and the relationship neff = (sum wi)^2 / sum(wi^2). That record is sufficient to rebuild this specific kish effective sample size calculation.
Does kish effective sample size establish a causal or population conclusion?
No. The displayed kish effective sample size value is conditional on the entered data and named method. The kish effective sample size design, measurement process, and assumptions determine what can be concluded beyond those values.
How should kish effective sample size be rounded?
Keep the unrounded kish effective sample size for subsequent arithmetic, then report only the precision supported by the source measurements and the decision context. Extra digits in kish effective sample size do not correct sampling or model error.
Which input deserves the closest boundary check?
For kish effective sample size, start with survey weights. Confirm the kish effective sample size units and allowed domain because a valid-looking entry can still describe the wrong statistical setup.