Normal probability plot
Compare observed quantiles with a fitted or specified normal reference using explicit plotting positions.
Paste comma-separated CSV with a header and 2 to 2000 complete rows. Use dot decimals with at most six decimal places and values from -1000000000 to 1000000000. No missing values are silently dropped. Extra columns are used only to preserve the original input in reports. Calculations use the required columns only. Processing stays on your device. Results use floating-point arithmetic. Charts show the supplied sample, not proof about a population. Use a value column. Compare observations with a normal reference fitted from sample mean and sample SD, or a normal reference with a specified mean from -1000000000 to 1000000000 and SD from 0.000001 to 1000000000. Plotting positions are Hazen (rank-0.5)/n. A fitted reference uses the same data and is not an independent validation. A constant sample has zero fitted spread. The plot is a visual diagnostic, not a normality test, p-value or proof of a normal population. Search and sort change this comparison only. Copy result copies the summary. Download JSON or CSV for all observations, results, settings and method assumptions. Download SVG for the chart. One disposable worker per calculation, with a 10-second timeout. CSV supports up to 100 columns and 100000 cells through the shared parser. Paste comma-separated CSV with a header and 2 to 2000 complete rows. Use dot decimals with at most six decimal places and values from -1000000000 to 1000000000. No missing values are silently dropped. Extra columns are used only to preserve the original input in reports. Calculations use the required columns only. Processing stays on your device. Results use floating-point arithmetic. Charts show the supplied sample, not proof about a population. Use a value column. Compare observations with a normal reference fitted from sample mean and sample SD, or a normal reference with a specified mean from -1000000000 to 1000000000 and SD from 0.000001 to 1000000000. Plotting positions are Hazen (rank-0.5)/n. A fitted reference uses the same data and is not an independent validation. A constant sample has zero fitted spread. The plot is a visual diagnostic, not a normality test, p-value or proof of a normal population. Up to 1,000,000 characters. Review the settings before running.
Your result
How to use Normal probability plot
- Paste complete numeric CSV rows with the required headers. Preserve the original order for lag diagnostics.
- Choose the displayed method settings and review their assumptions.
- Read the chart and table, then download all observations, results and assumptions as JSON or CSV.
Useful next steps
Normal probability plot
Use a value column. Compare observations with a normal reference fitted from sample mean and sample SD, or a normal reference with a specified mean from -1000000000 to 1000000000 and SD from 0.000001 to 1000000000. Plotting positions are Hazen (rank-0.5)/n. A fitted reference uses the same data and is not an independent validation. A constant sample has zero fitted spread. The plot is a visual diagnostic, not a normality test, p-value or proof of a normal population.
Keep the complete data trail
Search and sort change this comparison only. Copy result copies the summary. Download JSON or CSV for all observations, results, settings and method assumptions. Download SVG for the chart. Group labels are preserved. Numeric CSV inputs use a decimal point; editable numeric settings also accept the selected language’s decimal notation.