Target: To check if the assumed μ_{0} is statistically correct, based on a sample average and sample standard deviation
Example1: A farmer calculated last year the average of the apples' weight in his apple orchard μ_{0} equals 17 kg, based on big sample
Current year he checked a small sample of apples and the sample average x equals 18 kg
Was the average of the apple's weight changed this year?
The population's distribution is Normal | |
The standard deviations of the population is unknown | |
Population expected mean is known |
Sample average of the population | |
Sample standard deviation of the population | |
Sample size of the population |
If you enter raw data, the tool will run the Shapiro-Wilk normality test and calculate outliers, as part of the test calculation.
Header:
You may change groups' name to the real names.
Data:
When entering data, press Enter after each value.
The number of observations must be identical in both groups. (difference = right - left)
You may copy data from Excel, Google sheets or any tool that separate data with Tab and Line Feed.
Copy the data, one block of 3 consecutive columns includes the header, and paste below.
x_{d}: | Average of differences | |
μ_{0}: | Expected difference, usually zero | |
n: | Sample size, number of pairs | |
S_{d}: | The standard deviation of the differences |
skewness: | ||
Normality pval: | Shapiro Wilk test | |
Outliers_{d} | count: ,based on the Tukey's fences method, k=1.5 |