Code for Paired t-Test: Before and After Treatment Comparison
R
# Generate example dataset.seed(456)before_treatment <- rnorm(20, mean = 140, sd = 10) after_treatment <- before_treatment - rnorm(20, mean = 5, sd = 4) # Perform a paired t-testpaired_t_test_result <- t.test(before_treatment, after_treatment, paired = TRUE)# Print the resultprint(paired_t_test_result) |
Output:
Paired t-test
data: before_treatment and after_treatment
t = 4.6673, df = 19, p-value = 0.0001679
alternative hypothesis: true mean difference is not equal to 0
95 percent confidence interval:
2.227111 5.848578
sample estimates:
mean difference
4.037844
- We generate example data for the blood pressure of patients before and after a drug treatment. The before_treatment and after_treatment vectors represent paired measurements.
- We perform a paired t-test using the t.test function with the paired = TRUE argument, indicating that the measurements are paired.
- The result includes the t-statistic, degrees of freedom, and p-value, allowing us to assess whether there is a significant difference in blood pressure before and after treatment.
- The p-value is exceptionally small, much smaller than the common significance level of 0.05. This suggests strong evidence against the null hypothesis, indicating that there is a statistically significant difference in the means before and after the treatment.
Differences Between two-sample, t-test and paired t-test
Statistical tests are essential tools in data analysis, helping researchers make inferences about populations based on sample data. Two common tests used to compare the means of different groups are the two-sample t-test and the paired t-test. Both tests are based on the t-distribution, but they have distinct use cases and assumptions. In this article, we’ll explore the differences between these two tests in R, when to use each one, and how to conduct them in practice.
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