A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups.
Note: When the output says p-value => 4.903e-08 this also means <0.0001.
packages <- c("tidyverse", "ggpubr", "ggcorrplot")
packages <- lapply(packages, FUN = function(x) {
if(!require(x, character.only = TRUE)) {install.packages(x)
library(x, character.only = TRUE)}})
data <- ToothGrowth
t.test(df$len ~ df$supp)
plot <- ggboxplot(df, x = "supp", y = "len", color = "supp", palette = "jco", add = "jitter") +
stat_compare_means() # Add p-value
plot
t.test(df$len, df$dose)
plot <- ggboxplot(df, x = "len", y = "dose", color = "supp", palette = "jco", add = "jitter") +
stat_compare_means() # Add p-value
plot
compare_means(len ~ supp, data = df, paired = TRUE)
ggpaired(df, x = "supp", y = "len", color = "supp", line.color = "gray", line.size = 0.4, palette = "jco")+stat_compare_means(paired = TRUE)
t.test(df$len, df$dose, paired=TRUE)
Ho: mu=3
t.test(df$len, mu=3)
You can use the var.equal = TRUE option to specify equal variances and a pooled variance estimate. You can use the alternative="less" or alternative="greater" option to specify a one tailed test.
The value 2.2e-16 actually means 2.2 X 10 ^ -16. It is just a way R prints numbers that are either too big or too small. As you said that if the P-Value is less than 0.05, we reject the null hypothesis in case of test with 95% confidence or 5% significance. In general, we reject the null hypothesis when the P-value is less then the level of significance of the test.And if you're doing at test with 95% confidence or 5% significance, P-value of 2.2 X 10 ^-16 is very less compared to 5%=0.05. So you will reject the null hypothesis.
