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Pearson vs Spearman Correlation Coefficient

Last Updated : 23 Jul, 2025

Key Differences between Pearson and Spearman Correlation Coefficient are -

AspectPearson Correlation CoefficientSpearman Correlation Coefficient
Type of RelationshipMeasures linear relationships between variables.Measures monotonic relationships, where variables move consistently in one direction (not necessarily linearly).
Data TypeWorks with continuous interval or ratio data.Suitable for ordinal, ranked, interval, or ratio data.
AssumptionsAssumes linearity and normal distribution of data.Does not require normality or linearity; works well with non-parametric data.
Sensitivity to OutliersSensitive to outliers, which can skew the correlation value.Resistant to outliers since it uses ranks instead of raw data.
Calculation MethodBased on covariance and standard deviations of raw values.Based on ranking the data points and calculating the difference in ranks.
Range of CoefficientRanges from -1 to 1 (negative, positive, or no linear correlation).Ranges from -1 to 1 (negative, positive, or no monotonic correlation).
Ideal Use CasesUse when the data follows a normal distribution and shows linear trends.Use for non-linear or ranked data, or when dealing with outliers.
Fields of ApplicationFinance, healthcare, machine learning (e.g., stock price correlation).Education, psychology, customer satisfaction surveys (e.g., rank-based analysis).
ExampleAnalyzing the relationship between height and weight of individuals.Assessing the relationship between study hours and exam ranks of students.

When to Use Each Coefficient

  • Use Pearson if:
    • Your data is continuous and normally distributed.
    • You expect a linear relationship between variables.
    • You are concerned about the precise strength of a linear association.
  • Use Spearman if:
    • Your data is ordinal, ranked, or non-normally distributed.
    • The relationship between variables is monotonic but not necessarily linear.
    • Your data includes outliers that could distort a Pearson analysis.

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