The paired samples t-test compares two related measurements, like before-after scenarios or matched pairs . It's used to determine if there's a significant difference between paired observations, making it perfect for pre-post studies or comparing methods on the same subjects.
To conduct the test, you calculate differences between pairs, compute mean and standard deviation, and use a formula to find the t-statistic . Comparing this to critical values helps decide if there's a significant difference. Confidence intervals provide a range for the true mean difference .
Paired Samples T-Test
Situations for paired samples t-test
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Compares two related or dependent samples (before-after measurements, matched pairs)
Determines if the mean difference between paired observations significantly differs from zero
Frequently used in pre-post study designs (weight before and after a diet program)
Compares two different methods on the same subjects (two blood pressure measurement techniques on patients)
Conducting and interpreting paired t-tests
Calculate differences between each pair of observations
Compute mean difference (d ˉ \bar{d} d ˉ ) and standard deviation of differences (s d s_d s d )
Calculate t-statistic using formula: t = d ˉ s d / n t = \frac{\bar{d}}{s_d / \sqrt{n}} t = s d / n d ˉ
n n n represents number of paired observations
Determine degrees of freedom (df) = n − 1 n - 1 n − 1
Compare calculated t-value to critical t-value at chosen significance level and degrees of freedom
If calculated t-value > critical t-value, reject null hypothesis
Significant difference exists between paired observations
If calculated t-value ≤ critical t-value, fail to reject null hypothesis
Insufficient evidence to suggest significant difference between paired observations
Confidence intervals for paired differences
Provides range of plausible values for true mean difference
Formula: d ˉ ± t α / 2 , n − 1 ⋅ s d n \bar{d} \pm t_{\alpha/2, n-1} \cdot \frac{s_d}{\sqrt{n}} d ˉ ± t α /2 , n − 1 ⋅ n s d
d ˉ \bar{d} d ˉ represents mean difference
t α / 2 , n − 1 t_{\alpha/2, n-1} t α /2 , n − 1 represents critical t-value at chosen confidence level and degrees of freedom
s d s_d s d represents standard deviation of differences
n n n represents number of paired observations
Interpretation: ( 1 − α ) (1 - \alpha) ( 1 − α ) % confidence that true mean difference falls within calculated interval
If confidence interval excludes zero, suggests significant difference between paired observations (systolic blood pressure before and after medication)
Assumptions of paired samples t-tests
Differences between paired observations approximately normally distributed
Assess normality using histogram or normal probability plot
Test robust to normality violations for large sample sizes (n > 30)
Paired observations independent of each other
Each pair should not influence other pairs (multiple measurements on same patient)
Data continuous and measured on interval or ratio scale (temperature in ℃, weight in kg)
No significant outliers in differences between paired observations
Outliers can heavily influence test results (extremely large weight loss in diet study)