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ConsciousnessLearn · Explore · Joseph Anady

Reported two-sample t statistic

The mean difference is divided by its estimated standard error, using the sum of the two sample-mean variances. The source’s value near 348.79 agrees with the recovered descriptive calculation. Its magnitude does not remove the need to account for serial dependence,…

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eq-721a8d701eb15emathematical expression
t=xˉ2−xˉ1s12n1+s22n2=0.66160.000121000+0.060021000=348.79t = \frac{\bar{x}_2 - \bar{x}_1}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}} = \frac{0.6616}{\sqrt{\frac{0.0001^2}{1000} + \frac{0.0600^2}{1000}}} = 348.79

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The mean difference is divided by its estimated standard error, using the sum of the two sample-mean variances. The source’s value near 348.79 agrees with the recovered descriptive calculation. Its magnitude does not remove the need to account for serial dependence, changing sample cadence, and the fact that these are samples from one before-and-after sequence.

Terms & definitions

TermMeaning in this source
t

Reported standardized test statistic

s_1

Before sample deviation

s_2

After sample deviation

n_1

Before sample count

n_2

After sample count

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
\end{equation}

\textbf{t-statistic Calculation:}
\begin{equation}
t = \frac{\bar{x}_2 - \bar{x}_1}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}} = \frac{0.6616}{\sqrt{\frac{0.0001^2}{1000} + \frac{0.0600^2}{1000}}} = 348.79
\end{equation}

\end{document}
Exact original notation
t = \frac{\bar{x}_2 - \bar{x}_1}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}} = \frac{0.6616}{\sqrt{\frac{0.0001^2}{1000} + \frac{0.0600^2}{1000}}} = 348.79

Related builds

Sources & provenance

01
manuscript.tex
line 835 · Sep 22, 2026
t = \frac{\bar{x}_2 - \bar{x}_1}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}} = \frac{0.6616}{\sqrt{\frac{0.0001^2}{1000} + \frac{0.0600^2}{1000}}} = 348.79
02
manuscript.tex
lines 832–834
t = \frac{\bar{x}_2 - \bar{x}_1}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}} = \frac{0.6616}{\sqrt{\frac{0.0001^2}{1000} + \frac{0.0600^2}{1000}}} = 348.79
03
manuscript.tex
lines 834–836 · Sep 22, 2026
t = \frac{\bar{x}_2 - \bar{x}_1}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}} = \frac{0.6616}{\sqrt{\frac{0.0001^2}{1000} + \frac{0.0600^2}{1000}}} = 348.79
04
manuscript.tex
lines 832–834
t = \frac{\bar{x}_2 - \bar{x}_1}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}} = \frac{0.6616}{\sqrt{\frac{0.0001^2}{1000} + \frac{0.0600^2}{1000}}} = 348.79

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