Illustrates how the sampling distribution of the mean approaches normality as sample size increases.
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Enter Population Mean (μ), Population Standard Deviation (σ), and Sample Size (n) in the units shown.
Read Z-score (for a specific value - not calculated here, this is for conceptual understanding), Mean of the Sampling Distribution, and Standard Error of the Mean in the results panel. Results update as you change the inputs.
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Population Mean (μ): 50; Population Standard Deviation (σ): 5; Sample Size (n): 10
Z-score (for a specific value - not calculated here, this is for conceptual understanding): 0; Mean of the Sampling Distribution: 50; Standard Error of the Mean: 1.58113883
Population Mean (μ): 50; Population Standard Deviation (σ): 5; Sample Size (n): 100
Z-score (for a specific value - not calculated here, this is for conceptual understanding): 0; Mean of the Sampling Distribution: 50; Standard Error of the Mean: 0.5
Population Mean (μ): 50; Population Standard Deviation (σ): 20; Sample Size (n): 10
Z-score (for a specific value - not calculated here, this is for conceptual understanding): 0; Mean of the Sampling Distribution: 50; Standard Error of the Mean: 6.32455532
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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).