Browser-local processing

Bland-Altman Plot

Paste two paired numeric columns and Micro Tools Matrix will draw a browser-local Bland-Altman agreement plot with bias, standard deviation, and limits of agreement.

Local

Input

Input

Comma, tab, space, or semicolon separators are supported; headers and blank rows are ignored.

Ready: enter at least 3 paired rows to draw the chart.

Result

Output

Samples

0

Bias

—

Upper limit

—

Lower limit

—

The Bland-Altman plot is a practical way to compare two measurement methods and inspect agreement beyond a simple correlation coefficient. This free online developer tool from Micro Tools Matrix lets you paste two paired numeric columns, then calculates the mean of each pair, the difference between methods, the average bias, the standard deviation of differences, and the 95% limits of agreement. Everything runs with browser-local processing: your numbers stay in page memory, no upload is required, and no backend service receives the dataset. The tool is useful for experiment review, medical measurement checks, instrument calibration, quality control, and quick A/B method comparison. You can switch between a clean scatter view and a connected view, copy a localized statistical summary, or download the chart image for drafts and reports. It is intentionally lightweight, private, and fast, matching the Micro Tools Matrix approach to small, reliable utilities that work directly in modern browsers.

Practical guide

Bland-Altman Plot

Compare two measurement methods by plotting mean values against differences without uploading lab, product, or experiment data.

How to use

  1. Paste two paired numeric columns, one pair per row.
  2. Generate the chart to compute bias, standard deviation, and 95% limits of agreement.
  3. Inspect outliers or proportional bias visually before exporting an image.
  4. Copy results into a report, spreadsheet, or validation note.

Common use cases

  • Comparing two sensors, medical measurement methods, manual vs automated readings, or lab instruments.
  • Checking agreement between model output and reference values during validation.
  • Creating a quick exploratory plot before moving data into a heavier statistics package.

Privacy note

Measurement pairs remain in the browser. This is important when comparing sensitive experiment, QA, or student data.

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