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Spearman and Pearson correlation

From RankGear.wiki

Pearson correlation measures the strength of a straight-line relationship between two sets of data, while Spearman's rank correlation measures how well two sets of ranks agree, without assuming a straight line[1][2] . Both range from -1 to 1[1][2] . SEO tools such as RankGear compute both for each page factor against ranking position, and report the stronger as a "Best of Both" value[3] .

Key facts. Pearson's r measures linear correlation and Spearman's rho measures rank-order (monotonic) correlation; both run from -1 to 1, and Spearman is less sensitive to strong outliers[1][2] .

Overview

Correlation coefficients are the basic statistic behind ranking-factor studies, which ask whether a page feature tends to move with search position[4] . The two most common measures behave differently on the small, uneven data sets that search results produce, which is why RankGear calculates both[3] . What these numbers can and cannot show is covered in Correlation and causation in SEO.

Pearson correlation

Wikipedia describes the Pearson correlation coefficient, also known as Pearson's r or the Pearson product-moment correlation coefficient, as "the ratio between the covariance of two variables and the product of their standard deviations," which always falls between -1 and 1[1] . Unlike covariance, it has no units, so it can compare associations between variables measured in different units[1] . It can only reflect a linear correlation and ignores many other types of relationship[1] . It was developed by Karl Pearson from an idea introduced by Francis Galton in the 1880s, with the formula derived and published by Auguste Bravais in 1844[1] .

RankGear's glossary notes that a factor with "a real but curved association can post a weak coefficient even when it matters"[5] .

Spearman rank correlation

Spearman's rank correlation coefficient, named after Charles Spearman and often written as the Greek letter rho, indicates how strongly two sets of ranks are correlated[2] . Wikipedia's example is a statistician checking whether people who rank high in sprinting also rank high in long-distance running[2] . A Spearman correlation of 1 results when two variables are monotonically related, even if the relationship is not linear[2] .

When data are roughly elliptically distributed with no prominent outliers, the two coefficients give similar values; Spearman is less sensitive to strong outliers "because Spearman's ρ limits the outlier to the value of its rank"[2] . RankGear's glossary describes calculating it by ranking the pages in a results set twice, once by factor value and once by position, then measuring how closely the two orderings agree[6] .

Using correlation with ranking positions

The sign of a ranking correlation depends on how position is coded. RankGear's Pearson glossary entry notes that a negative sign "is common and expected" because a lower position number means a better rank, so a factor that rises as pages climb produces a negative r[5] . Its example is a word count factor with a Pearson r of -0.71, meaning longer pages tend to sit higher[5] . RankGear's Spearman entry, by contrast, describes a positive value as higher factor values accompanying better positions[6] . Readers should check which convention a report uses before interpreting a sign.

RankGear's Best of Both value is the stronger of the two coefficients for each factor[3] . Its example is a factor with a Pearson correlation of 0.28 and a Spearman correlation of 0.51, where the higher-ranking pages "still order cleanly by length"; RankGear reports 0.51[3] . The glossary acknowledges that because it takes the stronger of two methods, the figure "flatters the factor slightly by design"[3] . Correlations are one input to the Analysis Run.

Significance and sample size

Search results give small samples. RankGear's glossary defines sample size as the count of ranking pages successfully measured and included in a statistic, not the number of URLs requested[7] . Its example is a top ten in which two pages block the fetch and one returns an error, leaving a sample size of seven[7] . Pages that could not be measured are dropped rather than counted as zero[7] .

RankGear applies a Significance Threshold, the minimum absolute correlation a factor must reach before it is treated as statistically supported in the current sample[8] . Because the threshold uses the absolute value, strong negative relationships can clear it as well as positive ones[8] . In the glossary's example, with a 12-result set and a threshold of 0.30, a word count factor at 0.52 qualifies while an anchor text factor at 0.19 is filtered out[8] . The glossary warns that "a factor can cross the threshold on a handful of pages and drop below it on the next run"[8] .

Best-of-both correlation in RankGear

RankGear reports Spearman and Pearson values for each factor in its Results table and workbook Overview[6][5] . The Best of Both value appears in the workbook Overview, where factors are ranked by signal strength, and in the LSI Keywords view[3] . The glossary advises reading it as "the most favorable honest reading of that factor, not a guaranteed effect," and weighing it against sample size[3] . For the targets these correlations feed into, see Deficits and target strategies.

Common questions

What is the difference between Spearman and Pearson correlation?

Pearson measures the strength of a linear relationship between two variables[1] . Spearman measures how well two sets of ranks agree, so it captures any monotonic relationship and is less sensitive to strong outliers[2] .

Why use Spearman for rankings?

Search positions are ranks, and Spearman is designed for situations where the ranking of a data set is important[2] . RankGear notes that a single extreme page will not distort it the way it can distort a value-based measure[6] .

What does a negative correlation mean for ranking position?

If position is coded as a number where 1 is best, a factor associated with better rankings produces a negative correlation[5] . A value near zero means little consistent relationship in that result set[6] .

How many results are needed for a meaningful correlation?

No fixed number is given, but RankGear's glossary warns that coefficients from a handful of URLs can swing widely and that larger samples give more stable statistics[6][7] . Its significance threshold filters out correlations too weak to separate from noise[8] .

See also

References

  1. ^ a b c d e f g h i j k "Pearson correlation coefficient". Wikipedia. Retrieved November 2026.
  2. ^ a b c d e f g h i j k l m "Spearman's rank correlation coefficient". Wikipedia. Retrieved November 2026.
  3. ^ a b c d e f g "Best-of-both correlation". RankGear knowledge base. Retrieved November 2026.
  4. ^ "Correlation vs causation in SEO". RankGear (rankgear.com). Retrieved November 2026.
  5. ^ a b c d e "Pearson correlation". RankGear knowledge base. Retrieved November 2026.
  6. ^ a b c d e f "Spearman correlation". RankGear knowledge base. Retrieved November 2026.
  7. ^ a b c d "Sample size". RankGear knowledge base. Retrieved November 2026.
  8. ^ a b c d e "Significance threshold". RankGear knowledge base. Retrieved November 2026.