The point estimate calculator below gives the best possible estimate for the population proportion on the basis of the number of successes in ‘n’ trials.

Depending on the value of the sample proportion (“x/n”) there are different kinds of point estimates available such as the MLE estimate, Wilson point estimate, Jeffrey point estimate, Laplace point estimate, etc.

Best Estimate = **0.52500**

MLE Point Estimate = 0.52500

Wilson Point Estimate = 0.52342

Jeffrey Point Estimate = 0.52439

Laplace Point Estimate = 0.52381

This calculator uses the following logic to determine which point estimate is best to use:

If **x / n ≤ 0.5**, use the Wilson Point Estimate.

Otherwise, if **x / n < 0.9**, use the MLE Point Estimate.

Otherwise, if **x / n < 1.0**, use the smaller of the Jeffrey Point Estimate or the Laplace Point Estimate.

Otherwise, if **x / n = 1.0**, use the Laplace Point Estimate.

A point estimate is a single numerical value obtained using a sample from the entire population that is used to approximate a parameter (Eg: Population Mean, Population Proportion). The numerical value which is obtained using the sample values is called a statistic (Eg: Sample Mean, Sample Proportion).

A point estimate must have the following four characteristics to be a good estimate:

- Unbiasedness.
- Consistency.
- Efficiency.
- Sufficiency.