Calculates the range (+/-) around a sample statistic (like mean or proportion) that likely contains the true population parameter.
Start with your values, or try a preset.
Enter Confidence Level (%), Sample Size (n), Population Proportion (p) - if applicable, otherwise leave blank, and Sample Standard Deviation (σ) - if applicable, otherwise leave blank in the units shown.
Read Z-score, Margin of Error (Mean), Margin of Error (Proportion), and Margin of Error in the results panel. Results update as you change the inputs.
Choose a preset to load an example, then adjust the values for your situation.
See the inputs and their calculated results.
Confidence Level (%): 95 %; Sample Size (n): 100; Population Proportion (p) - if applicable, otherwise leave blank: 0.5; Sample Standard Deviation (σ) - if applicable, otherwise leave blank: 0
Z-score: 1.96; Margin of Error (Mean): 0; Margin of Error (Proportion): 0.098; Margin of Error: 0.098
Confidence Level (%): 99 %; Sample Size (n): 200; Population Proportion (p) - if applicable, otherwise leave blank: 0.2; Sample Standard Deviation (σ) - if applicable, otherwise leave blank: 0
Z-score: 2.58; Margin of Error (Mean): 0; Margin of Error (Proportion): 0.07297341982; Margin of Error: 0.07297341982
Confidence Level (%): 90 %; Sample Size (n): 500; Population Proportion (p) - if applicable, otherwise leave blank: 0; Sample Standard Deviation (σ) - if applicable, otherwise leave blank: 10
Z-score: 1.645; Margin of Error (Mean): 0.7356663646; Margin of Error (Proportion): 0; Margin of Error: 0.7356663646
Use calculators for algebra, geometry, trigonometry, probability, statistics, averages, z-scores, tests, and graphing tasks.
Continue with another tool on a related topic.
Performs a t-test (one-sample, two-sample, or paired) to compare means and determine statistical significance.
Finds the Z-score corresponding to a given cumulative probability (percentile) on a normal distribution.
Calculates the Chi-Square statistic and p-value for goodness-of-fit or test of independence. It requires observed and expected frequencies or values.
Identifies potential outliers in a dataset using the IQR * 1.5 rule (calculating fences).