Paired sample t test  overview
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Paired sample $t$ test  Pearson correlation 


Independent variable  Variable 1  
2 paired groups  One quantitative of interval or ratio level  
Dependent variable  Variable 2  
One quantitative of interval or ratio level  One quantitative of interval or ratio level  
Null hypothesis  Null hypothesis  
H_{0}: $\mu = \mu_0$
Here $\mu$ is the population mean of the difference scores, and $\mu_0$ is the population mean of the difference scores according to the null hypothesis, which is usually 0. A difference score is the difference between the first score of a pair and the second score of a pair.  H_{0}: $\rho = \rho_0$
Here $\rho$ is the Pearson correlation in the population, and $\rho_0$ is the Pearson correlation in the population according to the null hypothesis (usually 0). The Pearson correlation is a measure for the strength and direction of the linear relationship between two variables of at least interval measurement level.  
Alternative hypothesis  Alternative hypothesis  
H_{1} two sided: $\mu \neq \mu_0$ H_{1} right sided: $\mu > \mu_0$ H_{1} left sided: $\mu < \mu_0$  H_{1} two sided: $\rho \neq \rho_0$ H_{1} right sided: $\rho > \rho_0$ H_{1} left sided: $\rho < \rho_0$  
Assumptions  Assumptions of test for correlation  

 
Test statistic  Test statistic  
$t = \dfrac{\bar{y}  \mu_0}{s / \sqrt{N}}$
Here $\bar{y}$ is the sample mean of the difference scores, $\mu_0$ is the population mean of the difference scores according to the null hypothesis, $s$ is the sample standard deviation of the difference scores, and $N$ is the sample size (number of difference scores). The denominator $s / \sqrt{N}$ is the standard error of the sampling distribution of $\bar{y}$. The $t$ value indicates how many standard errors $\bar{y}$ is removed from $\mu_0$.  Test statistic for testing H0: $\rho = 0$:
 
Sampling distribution of $t$ if H_{0} were true  Sampling distribution of $t$ and of $z$ if H_{0} were true  
$t$ distribution with $N  1$ degrees of freedom  Sampling distribution of $t$:
 
Significant?  Significant?  
Two sided:
 $t$ Test two sided:
 
$C\%$ confidence interval for $\mu$  Approximate $C$% confidence interval for $\rho$  
$\bar{y} \pm t^* \times \dfrac{s}{\sqrt{N}}$
where the critical value $t^*$ is the value under the $t_{N1}$ distribution with the area $C / 100$ between $t^*$ and $t^*$ (e.g. $t^*$ = 2.086 for a 95% confidence interval when df = 20). The confidence interval for $\mu$ can also be used as significance test.  First compute the approximate $C$% confidence interval for $\rho_{Fisher}$:
Then transform back to get the approximate $C$% confidence interval for $\rho$:
 
Effect size  Properties of the Pearson correlation coefficient  
Cohen's $d$: Standardized difference between the sample mean of the difference scores and $\mu_0$: $$d = \frac{\bar{y}  \mu_0}{s}$$ Cohen's $d$ indicates how many standard deviations $s$ the sample mean of the difference scores $\bar{y}$ is removed from $\mu_0.$ 
 
Visual representation  n.a.  
  
Equivalent to  Equivalent to  
 OLS regression with one independent variable:
 
Example context  Example context  
Is the average difference between the mental health scores before and after an intervention different from $\mu_0 = 0$?  Is there a linear relationship between physical health and mental health?  
SPSS  SPSS  
Analyze > Compare Means > PairedSamples T Test...
 Analyze > Correlate > Bivariate...
 
Jamovi  Jamovi  
TTests > Paired Samples TTest
 Regression > Correlation Matrix
 
Practice questions  Practice questions  