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Limits of AgreementFree in-browser calculator

Bland-Altman Method Comparison Analyzer.

Upload paired method data, get limits of agreement with confidence intervals, proportional bias detection, and publication-ready plots. 100% client-side — your data never leaves your browser.

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Validated2026-03-25
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When to use

  • Comparing two measurement methods for agreement (not just correlation)
  • Validating a new method against a reference/gold standard
  • Assessing inter-rater or inter-instrument reliability
  • Detecting proportional bias between methods
  • Publication-ready Bland-Altman plots with confidence intervals

Do not use for

  • Comparing more than two methods simultaneously (use repeated-measures ANOVA)
  • Correlation analysis only (Bland-Altman specifically tests agreement, not correlation)
  • Categorical or ordinal data (requires continuous measurements)

Correlation is not agreement

Two methods can have r = 0.99 and still disagree by a clinically significant amount. Correlation measures the strength of a linear relationship, not whether two methods give the same result. Bland and Altman's original 1986 paper was written precisely because this mistake was — and remains — ubiquitous.

Check for proportional bias before interpreting limits of agreement

If disagreement between methods grows (or shrinks) with measurement magnitude, flat limits of agreement are misleading. This tool automatically runs a regression of differences on means. If the slope is significantly non-zero (p < 0.05), consider using percentage or ratio mode instead.

Minimum 30 paired observations for meaningful results

Bland and Altman recommend at least 40 pairs, and 100+ for narrow confidence intervals on the limits of agreement. With fewer than 30 pairs, the 95% CIs on the LoA are so wide that the analysis has little practical value.

Differences must be approximately normally distributed

The limits of agreement (mean ±\pm 1.96 ×\times SD) assume normality. This tool runs a Shapiro-Wilk test on the differences automatically. If normality is rejected, consider log-transforming the data or using the ratio/percentage mode, which often normalizes multiplicative error.

Use ratio mode for data with multiplicative error structure

When scatter in the Bland-Altman plot fans out (increases with magnitude), the error is multiplicative, not additive. Switching to ratio mode (Method 1 / Method 2) or percentage mode stabilizes the variance and produces more valid limits of agreement.

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Method

Limits of agreement (mean difference ±\pm 1.96 ×\times SD) with 95% confidence intervals computed per Bland & Altman (1999). Proportional bias assessed by linear regression of differences on means. Normality of differences tested by Shapiro-Wilk.

2

Validated

Last validated 2026-03-25. Calculations are designed for planning and documentation support; verify procurement decisions against manufacturer specifications or institutional SOPs.

3

How to cite

How to Cite

ConductScience Bland-Altman Method Comparison Analyzer (1.0.0). ConductScience, Inc. 2026. Available at: https://conductscience.com/tools/bland-altman-analyzer

Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet. 1986;327(8476):307-310. doi:10.1016/S0140-6736(86)90837-8

What Is a Bland-Altman Plot?

A Bland-Altman plot (also called a difference plot or Tukey mean-difference plot) is a graphical method for comparing two measurement techniques. Introduced by J. Martin Bland and Douglas G. Altman in their landmark 1986 *Lancet* paper, it has become the standard method for assessing agreement between two quantitative methods of measurement.

Unlike correlation or regression analysis, the Bland-Altman method directly visualizes and quantifies the differences between two methods across the range of measurements. Two methods can be highly correlated (r = 0.99) yet disagree by a clinically significant amount.

How it works: For each paired measurement, the plot displays the mean of the two methods on the x-axis and the difference between them on the y-axis. Three horizontal reference lines mark the mean difference (bias) and the limits of agreement.

How to Interpret Bland-Altman Results

Mean Difference (Bias): The average difference between Method 1 and Method 2. A bias near zero indicates no systematic tendency for one method to read higher or lower.
Limits of Agreement (LoA): The range within which 95% of differences are expected to fall. Whether acceptable depends on clinical context.
Confidence Intervals on LoA: CI bands show the uncertainty in the LoA estimates.
Proportional Bias: If the regression slope is significantly non-zero (p < 0.05), disagreement changes with measurement magnitude.
Sample Size: At least 40 pairs recommended; 100+ for narrow CIs.

When to Use Bland-Altman Analysis

Use Bland-Altman analysis when: - Validating a new method against a reference method - Comparing two instruments measuring the same quantity - Assessing inter-rater or intra-rater agreement - Evaluating point-of-care devices against lab standards - FDA 510(k) submissions or IVD validation
Do NOT use when: Comparing different quantities, data are categorical, or measurements are not paired.
Common applications: Clinical chemistry, diagnostics, medical imaging, respiratory medicine, cardiology, and sports science.

Difference, Ratio, and Percentage Modes

Difference Mode (default): d = Method 1 − Method 2. Use when variability is constant across the range.
Ratio Mode: r = Method 1 / Method 2. Use for data spanning several orders of magnitude.
Percentage Difference Mode: %d = (d / mean) × 100. Use when absolute difference scales proportionally with magnitude.
How to choose: If the Breusch-Pagan test is significant in Difference mode, try Ratio or Percentage mode.

Statistical Methodology

Core method: Bland JM, Altman DG. "Statistical methods for assessing agreement between two methods of clinical measurement." *The Lancet*, 1986;327(8476):307-310. PMID: 2868172.
Confidence intervals: SE = √(3s²/n) for LoA, with t-distribution critical values.
Proportional bias: Linear regression of differences on means with t-test for slope.
Normality: Shapiro-Wilk test on differences.
Heteroscedasticity: Breusch-Pagan test on squared residuals.

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