Correlation Significance Calculator

When to use

• Use Pearson correlation for linear association between paired quantitative variables and Spearman for monotonic rank association.
• Use it to describe association strength and direction before more complete modelling.
• Use partial correlation or regression when adjustment for additional variables is required.

How to use

1. Enter matched x,y pairs or select two numeric CSV columns.
2. Choose Pearson or Spearman according to scale, pattern, and analysis plan.
3. Calculate the coefficient, p-value, interval where available, and scatterplot.
4. Inspect outliers, nonlinearity, ties, range restriction, and dependence before reporting.

Inputs

Paired x,y values

Every row must contain measurements from the same observational unit.

Correlation method

Pearson uses raw values for linear association; Spearman uses ranks for monotonic association.

Selected CSV columns

Two numeric columns aligned by the same dataset rows.

Alpha

The prespecified threshold for testing a zero population association.

Notation

Interpretation

• The coefficient ranges from -1 to 1 and combines direction with the strength of the selected association form.
• A p-value tests evidence against zero association under assumptions; it does not measure practical importance.
• Correlation cannot establish causation and may be driven by confounding, selection, trends, or a few influential points.

Effect-size guidance

• Read the coefficient with its interval and scatterplot.
• Do not apply generic strength labels without subject context and measurement reliability.
• A restricted variable range can attenuate an otherwise meaningful relationship.

Worked examples

Assumptions

• Inputs are finite numeric values after parsing, with missing or invalid values handled according to the selected workflow.
• Pairs are independent and matched by row.
• Pearson assumes linear association; Spearman uses rank-order association.

Practical guidance

• Verify pair alignment after missing-value handling.
• Plot the data and label influential observations for source review.
• Use a designed causal analysis before making intervention claims.

Common mistakes

• Inferring causation from correlation.
• Testing constant columns.

Limitations

• Partial correlation and regression adjustment are out of scope.
• Fisher intervals are approximate.

Related workflow links