Pearson correlation test

  • Assumptions to check 

    • Independence of observations

    • The distribution of data in the underlying population from each of the samples is derived is normal.Normal distribution (testable by e.g. Kolmogorov–Smirnov test, Shapiro–Wilk test, the Anderson-Darling test, Q-Q plot)

    • Linearity (e.g inspecting scatterplot)

  • Report design in Methods section

    • Report on variables. 

    • Name the statistical package or program used in the analysis.

  • Report statistics in Results section:

    •  correlation coefficient r, p-value, t-statistics

  • Example: 

    • Methods section: To evaluate the correlation between factors (height and weight) the parametric Pearson correlation test was used. The analysis was performed in R software (RRID:SCR_001905).

    • Results section: The parametric Pearson correlation analysis was used, and it showed a high and significant positive correlation between height and weight (r(98) = .63, p < .001, t=2.35 ). The scatterplot, with a linear trend line, showed no violation of the linearity assumption and no significant outliers. The Shapiro-Wilk normality test results indicated that both weight (W = .99, p = .454) and height (W = .98, p = .165) variables fit the normal distribution.