Paired sample t test - overview
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Paired sample $t$ test | Pearson correlation | Paired sample $t$ test |
You cannot compare more than 3 methods |
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Independent variable | Variable 1 | Independent variable | |
2 paired groups | One quantitative of interval or ratio level | 2 paired groups | |
Dependent variable | Variable 2 | Dependent variable | |
One quantitative of interval or ratio level | One quantitative of interval or ratio level | One quantitative of interval or ratio level | |
Null hypothesis | Null hypothesis | Null hypothesis | |
H0: $\mu = \mu_0$
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. | H0: $\rho = \rho_0$
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. | H0: $\mu = \mu_0$
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. | |
Alternative hypothesis | Alternative hypothesis | Alternative hypothesis | |
H1 two sided: $\mu \neq \mu_0$ H1 right sided: $\mu > \mu_0$ H1 left sided: $\mu < \mu_0$ | H1 two sided: $\rho \neq \rho_0$ H1 right sided: $\rho > \rho_0$ H1 left sided: $\rho < \rho_0$ | H1 two sided: $\mu \neq \mu_0$ H1 right sided: $\mu > \mu_0$ H1 left sided: $\mu < \mu_0$ | |
Assumptions | Assumptions of test for correlation | Assumptions | |
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Test statistic | 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: $\rho = 0$:
| $t = \dfrac{\bar{y} - \mu_0}{s / \sqrt{N}}$
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 $\mu_0$. | |
Sampling distribution of $t$ if H0 were true | Sampling distribution of $t$ and of $z$ if H0 were true | Sampling distribution of $t$ if H0 were true | |
$t$ distribution with $N - 1$ degrees of freedom | Sampling distribution of $t$:
| $t$ distribution with $N - 1$ degrees of freedom | |
Significant? | Significant? | Significant? | |
Two sided:
| $t$ Test two sided:
| Two sided:
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$C\%$ confidence interval for $\mu$ | Approximate $C$% confidence interval for $\rho$ | $C\%$ confidence interval for $\mu$ | |
$\bar{y} \pm t^* \times \dfrac{s}{\sqrt{N}}$
where the critical value $t^*$ is the value under the $t_{N-1}$ 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 $\rho$:
| $\bar{y} \pm t^* \times \dfrac{s}{\sqrt{N}}$
where the critical value $t^*$ is the value under the $t_{N-1}$ 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. | |
Effect size | Properties of the Pearson correlation coefficient | Effect size | |
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 $\mu_0.$ |
| 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. | Visual representation | |
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Equivalent to | Equivalent to | Equivalent to | |
| OLS regression with one independent variable:
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Example context | 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? | Is the average difference between the mental health scores before and after an intervention different from $\mu_0 = 0$? | |
SPSS | SPSS | SPSS | |
Analyze > Compare Means > Paired-Samples T Test...
| Analyze > Correlate > Bivariate...
| Analyze > Compare Means > Paired-Samples T Test...
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Jamovi | Jamovi | Jamovi | |
T-Tests > Paired Samples T-Test
| Regression > Correlation Matrix
| T-Tests > Paired Samples T-Test
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Practice questions | Practice questions | Practice questions | |