ThatAIguyA world of ideas

THE CONSCIOUSNESS ATLAS
Read. Experiment. Follow the evidence.

ConsciousnessLearn · Explore · Joseph Anady

Pooled sample standard deviation

Each sample variance is weighted by its degrees of freedom, the weighted variances are averaged, and the square root returns the original measurement units. This is the conventional equal-variance pooled estimator used in Cohen’s d. Both groups require more than one…

Research mathematicsdocumented formula
Learn with the Atlas

Learn to read this model

Start with the symbols, follow the operations, and check what the result means.

Open guided lesson
eq-840ca68fe78231mathematical expression
sp=(n1−1)s12+(n2−1)s22n1+n2−2s_p = \sqrt{\frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1+n_2-2}}

Read the equation

Each sample variance is weighted by its degrees of freedom, the weighted variances are averaged, and the square root returns the original measurement units. This is the conventional equal-variance pooled estimator used in Cohen’s d. Both groups require more than one observation for the displayed sample-variance calculation.

Terms & definitions

TermMeaning in this source
s_p

Pooled standard deviation

n_1

First sample size

n_2

Second sample size

s_1

First sample standard deviation

s_2

Second sample standard deviation

How it works

Read the definition from its right-hand expression to the quantity on the left. Its surrounding source records the intended input, state, or parameter context. Where the source leaves an operation unspecified, this page preserves that gap.

Assumptions & limits

Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.

Original context

Read the surrounding source passage
\textbf{Cohen's $d$ Calculation:}
\begin{equation}
d = \frac{\bar{x}_2 - \bar{x}_1}{s_p}
\end{equation}
where $s_p = \sqrt{\frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1+n_2-2}}$.

For our data:
\begin{align}
\bar{x}_1 &= 0.2499, \quad s_1 = 0.0001, \quad n_1 = 1000 \\
\bar{x}_2 &= 0.9115, \quad s_2 = 0.0600, \quad n_2 = 1000
\end{align}
Exact original notation
s_p = \sqrt{\frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1+n_2-2}}

Related builds

Sources & provenance

01
manuscript.tex
line 817 · Sep 22, 2026
s_p = \sqrt{\frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1+n_2-2}}
02
manuscript.tex
line 815
s_p = \sqrt{\frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1+n_2-2}}
03
manuscript.tex
line 817 · Sep 22, 2026
s_p = \sqrt{\frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1+n_2-2}}
04
manuscript.tex
line 815
s_p = \sqrt{\frac{(n_1-1)s_1^2 + (n_2-1)s_2^2}{n_1+n_2-2}}

Search equations, builds, manuscript text, and recovered sources.