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This point estimate calculator helps to determine the best guess of the population parameter. To find the point estimate, these calculations are based on no. of successes and trials along with the confidence level. Our tool automates the results in four different methods including Maximum Likelihood (MLE), Wilson, Laplace, and Jeffrey's.

**"A point estimate is the value that shows the most probable outcome of a variable"**

This value is taken from one or more samples to approximate an unknown population parameter. It is commonly used when data collection for an entire population is not a practical option.

There are four different point estimate formulas where each equation gives slightly different results and should be applied depending on the situation. This point estimate calculator selects the most applicable method by default and shows the results in all of them that are listed right below.

= x n

= (x + z^{2}/2) (n + z^{2})

= (x + 1) (n + 2)

= (x + 0.5) (n + 1)

After knowing the formulas, it is important to know on which basis we have selected the equation. Follow the below rules to do this:

- If
**MLE ≤ 0.5**- Use Wilson Estimation - If
**0.5 < MLE < 0.9**- Use Maximum Likelihood Estimation (MLE) - If
**MLE ≥ 0.9**- Choose the smaller value between Jeffrey and Laplace Estimations

Calculating point estimated value involves these steps:

✤ Estimate the number of trials or sample size

✤ Find the number of successes

✤ Use the appropriate formula according to the values

A basketball player takes 9 free throw shots and makes 4 of them. Calculate the best point estimate of his success rate with a 95% confidence interval.

**Given Values:**

- Number of successes = 4
- Number of Trials = 9
- Confidence Interval Level = 95%
- Z-Critical Value for 95% level = - 1.96

Solution (Step-by-Step)➥ MLE

= 4 9

= 0.4444

**➥ Laplace**

= 4 + 1 9 + 2

= 5 11

= 0.4545

**➥ Jeffrey**

= 4 + 0.5 9 + 1

= 4.5 10

= 0.45

**➥ Wilson**

= 4 + ((-1.96)^{2} / 2) 9 + (-1.96)^{2}

= 0.4611

Hence, the 0.4611 is the best point estimation as MLE ≤ 0.5

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