A point estimate represents our “best guess” of a population parameter.

For example, a sample mean can be used as a point estimate of a population mean.

Similarly, a sample proportion can be used as a point estimate of a population proportion. However, there are several ways to calculate the point estimate of a population proportion, including:

MLE Point Estimate: x / n

Wilson Point Estimate: (x + z^{2}/2) / (n + z^{2})

Jeffrey Point Estimate: (x + 0.5) / (n + 1)

Laplace Point Estimate: (x + 1) / (n + 2)

where x is the number of “successes” in the sample, n is the sample size or number of trials, and z is the z-score associated with the confidence level.

To find the best point estimate, simply enter in the values for the number of successes, number of trials, and confidence level in the boxes below and then click the “Calculate” button.

Best Estimate = 0.45695

MLE Point Estimate = 0.45161

Wilson Point Estimate = 0.45695

Jeffrey Point Estimate = 0.45313

Laplace Point Estimate = 0.45455

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.

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.