You might need to rely on your understanding of the data.
#Paired samples t test code
In the code below, the term (1subj) is the adjustment by subject to the interceptsthe term (u0i) above. Let’s check that this is true using our simulated data. So, the paired t-test will deliver exactly the same t-score as the above linear mixed model. What if my data isn’t nearly normally distributed If your sample sizes are very small, you might not be able to test for normality. But that is exactly the same standard deviation as the one used in the paired t-test.
A difference score is the difference between the first score of a pair and the second score of a pair. The paired t -test is also known as the dependent samples t -test, the paired-difference t -test, the matched pairs t -test and the repeated-samples t -test.
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. One quantitative of interval or ratio level Binomial test for a single proportion $z$ test for a single proportion $z$ test for the difference between two proportions Goodness of fit test Chi-squared test for the relationship between two categorical variables One sample $z$ test for the mean One sample $t$ test for the mean Paired sample $t$ test Two sample $z$ test Two sample $t$ test - equal variances not assumed Two sample $t$ test - equal variances assumed One way ANOVA Two way ANOVA Pearson correlation Regression (OLS) Logistic regression Mann-Whitney-Wilcoxon test Kruskal-Wallis test Sign test McNemar's test Cochran's Q test Marginal Homogeneity test / Stuart-Maxwell test Friedman test Wilcoxon signed-rank test One sample Wilcoxon signed-rank test Spearman's rho Since this is just an ordinary, one-sample t-test, with nothing special about it, the degrees of freedom are still N - 1.