On the Nonadditivity of Correlation Coefficients Part 2: Fisher Transformations


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This is Part 2 of a tutorial series on the nonadditivity of correlation coefficients. This time we discuss Fisher r-to-Z and Z-to-r transformations and the risks of arbitrarily implementing these.

Misusing the transformations can have a detrimental impact on significance testing, confidence intervals, reported standard errors of correlations, and meta-analysis.

The tutorial is available at




Simple Linear Regression & Correlation Calculator


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This is a new Minerazzi tool available now at


The tool does simple linear regression and correlation analyses, computing Spearman and Pearson correlation coefficients and other relevant statistics.

The companion default example was intentionally selected to illustrate that for rank data free from ties, Spearman and Pearson correlation coefficients are the same thing.


On the Nonadditivity of Correlation Coefficients Part 1: Pearson and Spearman Coefficients


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This is Part 1 of a tutorial series on the nonadditivity of correlation coefficients. We demonstrate why it is not possible to arithmetically add, subtract, and average Pearson’s r or Spearman’s rs.

The article is available at


07-04-2017 update: In page 1 the line for the Beta1 should read “is the slope of a simple linear regression model”. My fault. Fixed today along with few other nuances.

Enjoy it!

t,p, & Effect Size Estimator


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This is a new Minerazzi tool, available now at


This tool estimates t-scores from p-values and vice versa for a given number of degrees of freedom υ. Just enter a (t,υ) or (p,υ) pair and this tool will solve for the missing term.

The tool also estimates the statistics that one would obtain if the computed estimates correspond to a set of paired variables (x, y), or to effect sizes from any two samples of same sizes (n1 and n2); i.e. samples with same degrees of freedom (υ1 and υ2).

The tool’s page lists some interesting exercises and good references for effect size conversions and meta-analysis in general. Enjoy it!


Student’s t Table Generator


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This is a new tool available now at


This tool allows you to easily generate a customized table of Student’s t-values.

That is done by iteratively calling (i.e., looping) the very same algorithm that we use for our t-values Calculator.

This tool comes handy when you don’t have statistical t-tables around or are working with p and t values, or degrees of freedom, not available from such tables. Avoid pausing a problem for annoying linear interpolation workarounds!

Meta-Analysis Miner


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This is a new miner available now at


Find resources relevant to meta-analysis, effect sizes, and power analysis with this Minerazzi miner.

Build curated collections on these subjects.

The miner includes Bing and Google RSS News channels powered by our SPP tool.

t-values Calculator for Student’s t Hypothesis Testing


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The t-values Calculator is a new Minerazzi tool, available now at


The tool works by computing, through numerical approximation methods, the Inverse Cumulative Distribution Function or ICDF (also known as the Quantile Function, QF) of the Student’s t Distribution.

This new tool complements our p-values Calculator, described in


and available at


The t-values calculator comes handy when you don’t have around statistical t-tables or want to t-testing at confidence levels and degrees of freedom not listed in such tables. In fact, with a small change to the script, we can build another tool for the generation of t-tables at any confidence level and degrees of freedom as specified by a user.

Both tools will eventually be used to provide a third tool: The t-p Transformation tool that, as the name suggests, transforms CDF (t-to-p) into ICDF (p-to-t) results and vice versa.



p-values Calculator for Student’s t Hypothesis Testing


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The p-Values Calculator is a new Minerazzi tool that is available now at


Submitting a Student’s t value and degrees of freedom returns a p-value. This is a great tool for Student’s t hypothesis testing.

The tool works by numerically approximating the CDF (Cumulative Distribution Function). This is the integral of the PDF (Probability Distribution Function) of the Student’s t Distribution. The theory behind these calculations along with valuable references are given in the tool’s page.


The reverse process, computing t from a p-value is possible by inverting the CDF to compute the Quantile Function (QF), also known as the inverse CDF. Our (soon to be released) t-p Transformations tool computes both the CDF (t-to-p) and QF (p-to-t).

Updating Several Miners


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We have reindexed the following miners: