Sample mean or proportion point estimates, with standard error and an approximate interval.
A point estimate is a single best guess from the sample — it says nothing about precision without its standard error, shown alongside.
point estimate
How to use this tool
A list of numbers gives a sample mean; counts give a sample proportion.
Comma-separated values, or successes and trials.
The big number is the point estimate; the standard error says how wobbly it is.
The approximate 95% interval shows the plausible range for the true population value.
Formula & worked example
Sample 2, 4, 6, 8: the mean is 5, the sample standard deviation is 2.5820 and the standard error is 2.5820 ÷ √4 = 1.2910. For a proportion, 30 successes in 100 trials gives p̂ = 0.3000 with SE = 0.0458.
Limitations — read this before you rely on it
A point estimate is only as good as its sample: biased or tiny samples give confident-looking but wrong answers. The interval shown uses the normal approximation (1.96 × SE); for small samples use a t interval, and for proportions near 0 or 1 use a Wilson interval. It never proves the population value.
Frequently asked questions
A single number computed from a sample that estimates an unknown population value — like the sample mean estimating the population mean.
How much the estimate would typically vary from sample to sample. Bigger samples give smaller standard errors.
Because a point alone hides uncertainty: 5 ± 1.29 is far more honest than just 5.
√(p̂(1 − p̂)/n). It is largest at p̂ = 0.5 and shrinks as p̂ nears 0 or 1.
The mean uses all values but is pulled by outliers; the median resists them. This tool estimates the mean, the most common choice.