Paired-Sample T-Test This section presents the results of the traditional paired-sample T-test. Here, reports for all three alternative hypotheses are shown, but a researcher would typically choose one of the three before generating the output. All three tests are shown here for the purpose of...
Interpretational Issues Thus far, we compared 3 pairs of exams using 3 t-tests. A shortcoming here is that all 3 tests use the same tiny student sample. This increases the risk that at least 1 test is statistically significant just by chance. There's 2 basic solutions for this: apply a...
t.test(weight ~ group, data = my_data, paired = TRUE, alternative = "greater") Interpretation of the result Thep-valueof the test is 6.210^{-9}, which is less than the significance level alpha = 0.05. We can then reject null hypothesis and conclude that the average weight of the mic...
We’ll use the pipe-friendlyt_test()function [rstatix package], a wrapper around the R base functiont.test(). The results can be easily added to a plot using theggpubrR package. stat.test <- mice2.long %>% t_test(weight ~ group, paired =TRUE) %>% add_signifi...
Interpretation Statistical significance is determined by looking at the p-value. The p-value gives the probability of observing the test results under the null hypothesis. The lower the p-value, the lower the probability of obtaining a result like the one that was observed if the null hypothesis...
Further, we found clear advantages in pairing these two high-throughput techniques, as the data generated by each studies aids in the interpretation of the other. In addition, analysis of the paired data suggests potential follow-up studies that would be difficult to interpret with one assay in...
Interpretation By looking at the ‘Significantly different? (P<0.05)’ output, a ‘Yes’ is given which essentially means that our results are significantly different from each other. The exact p value is given next to ‘P value’. In this case a p value of < 0.0001 indicates a very sig...
Since the previous results showed that the level of genomic differences between paired P-R GBMs was positively correlated with TTR (Fig. 2C), we asked whether the TMB values of the P-R pairs were highly correlated with each other. As expected, Pearson’s correlation test revealed a signific...
coeff_t_test.m estimate_selection.m estimate_selection_parameter.m main_vPECA.m test.m Repository files navigation README vPECA vPECA is a Variants interpretation method by Paired Expression and Chromatin Accessibility data which can identify active and active selected regulatory elements and their ...
This analysis aligns with the results from Fig. 3 demonstrating a consistent and highly significant (p < 0.001 for the α- and α + β-chain models, p = 0.03 for β-chain, bootstrap test with 1000 repetitions) superior performance of NetTCR-2.0 over TCRdist, and likewis...