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Normal Probability Plot (QQ Plot) Maker

Sorts your data and pairs each value with the quantile expected under a normal distribution. The closer the points lie to the diagonal reference line, the more normal the data - and R² rates normality automatically.

1Data Table

Paste a table copied from Excel into a cell (Ctrl+V). Click a column header (A, B…) before pasting to use the first line as column names. Double-click a header to rename it, right-click to insert/delete rows and columns, and formulas such as =A1*2 work too.

2QQ Plot Options

Select data columns for the normal QQ plot:

How to make a normal probability plot

  1. Copy the measurement column you want to check, together with its header.
  2. Click the column header (A) in the data table and paste - the first line becomes the column name. You can also open a CSV or Excel file.
  3. Select the columns to check (several columns are overlaid for comparison).
  4. Press Draw and check how close the points are to the reference line, the R² and the automatic interpretation.

What is a normal probability plot (QQ plot)?

A normal probability plot sorts the data from smallest to largest and plots each value against the theoretical (standard normal) quantile expected for a sample of the same size from a normal distribution. If the data are normal, the points lie almost on a straight reference line.

It is widely used to check the normality assumption before analyses that rely on it, such as process capability (Cpk, Ppk), t-tests and control charts.

How it is calculated

The theoretical quantiles are Filliben's order-statistic medians converted to standard normal quantiles (the same method as scipy's probplot). The reference line is the least-squares line, and R² is the squared correlation between the theoretical quantiles and the observed values.

How to read a normal probability plot

  • The automatic interpretation calls the data very close to normal when R² is 0.98 or higher, and roughly normal when it is 0.9 or higher.
  • Ends that curve away above and below the line indicate heavy or light tails; curving to one side only indicates skewness.
  • Step-like gaps in the middle can mean a coarse measurement resolution or rounded values.

Frequently asked questions

Can I get a normality test p-value?

The probability plot checks normality visually. Normality tests with p-values, such as Anderson-Darling, are available in the statistics menu of the Qstat desktop program.

How many data points do I need?

The plot works from 2 points, but at least 20-30 are recommended to judge the shape reliably.

Can I compare several columns?

Yes. Select several columns to overlay them on one chart with an R² and interpretation for each.

Is my data sent to a server?

No. Everything is processed inside your browser and nothing is transmitted or stored.

Other chart tools

Histogram MakerSee the distribution, a fitted normal curve and Pp/Ppk against spec limits at once.Run Chart MakerConnect values in measurement order with a median line and control limits to follow the process.Box Plot MakerCompare medians, quartiles and outliers of several groups side by side.

Need process capability, control charts or DOE?

The Qstat desktop program offers about 250 analysis features: histograms and box plots plus process capability (Cp, Cpk), control charts, measurement system analysis (MSA), design of experiments (DOE), regression and machine learning.

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