One sample t test for the mean  overview
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One sample $t$ test for the mean  Pearson correlation 


Independent variable  Variable 1  
None  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  
$\mu = \mu_0$
$\mu$ is the unknown population mean; $\mu_0$ is the population mean according to the null hypothesis  $\rho = \rho_0$
$\rho$ is the unknown Pearson correlation in the population, $\rho_0$ is the correlation in the population according to the null hypothesis (usually 0)  
Alternative hypothesis  Alternative hypothesis  
Two sided: $\mu \neq \mu_0$ Right sided: $\mu > \mu_0$ Left sided: $\mu < \mu_0$  Two sided: $\rho \neq \rho_0$ Right sided: $\rho > \rho_0$ Left sided: $\rho < \rho_0$  
Assumptions  Assumptions of tests for correlation  

 
Test statistic  Test statistic  
$t = \dfrac{\bar{y}  \mu_0}{s / \sqrt{N}}$
$\bar{y}$ is the sample mean, $\mu_0$ is the population mean according to H0, $s$ is the sample standard deviation, $N$ is the sample size. 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 H0 were true  Sampling distribution of $t$ and of $z$ if H0 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 approximate $C$% confidence interval for $\rho_{Fisher}$:
Then transform back to get approximate $C$% confidence interval for $\rho$:
 
Effect size  Properties of the Pearson correlation coefficient  
Cohen's $d$: Standardized difference between the sample mean and $\mu_0$: $$d = \frac{\bar{y}  \mu_0}{s}$$ Indicates how many standard deviations $s$ the sample mean $\bar{y}$ is removed from $\mu_0$ 
 
Visual representation  n.a.  
  
n.a.  Equivalent to  
  OLS regression with one independent variable:
 
Example context  Example context  
Is the average mental health score of office workers different from $\mu_0$ = 50?  Is there a linear relationship between physical health and mental health?  
SPSS  SPSS  
Analyze > Compare Means > OneSample T Test...
 Analyze > Correlate > Bivariate...
 
Jamovi  Jamovi  
TTests > One Sample TTest
 Regression > Correlation Matrix
 
Practice questions  Practice questions  