# Set known parameters g_means <- c(0, 0) g_sds <- c(1, 1) g_samples <- c(50, 50) g_labels <- factor(rep(c("G1","G2"), times = g_samples)) # Generate simulated sample from normal distribution g1_data <- rnorm(n = g_samples[1], g_means[1], g_sds[1]) g2_data <- rnorm(n = g_samples[2], g_means[2], g_sds[2]) # Put into data frame sim_df <- data.frame(measure = c(g1_data, g2_data), group = g_labels) # Look at data plot(measure ~ group, data = sim_df) stat_output <- t.test(measure ~ group, data = sim_df) stat_output$p.value ### # So how do we run this repeatedly? # Use a for-loop: # Set the number of runs runs <- 100 # Pre-allocate loop output loop_out <- numeric(runs) # Example for-loop for (n in 1:runs) { loop_out[n] <- n^2 print(loop_out[n]) }