# Hello World! Here’s a Normal Distribution!

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This code simulates Normal(0,1), and this visualization shows smaller samples can vary much more than large samples from the true distribution. Maybe it’s not a fascinating picture although there is a deep mystery or two in there. Can we know the truth? Isn’t everything we know based on a sample? Is everything we believe, like these three `rnorm()`

, an incomplete story?

require(tidyverse) require(tidyr) random_simulations_1 <- tibble(rnorm(100000)) %>% gather %>% rename(distribution = key, observed = value) random_simulations_2 <- tibble(rnorm(1000)) %>% gather %>% rename(distribution = key, observed = value) %>% bind_rows(random_simulations_1) random_simulations <- tibble(rnorm(10)#, rnorm(100) #runif(100)#, #rhyper(100, 100, 50, 10), #rbinom(100, 10, .5) ) %>% gather %>% rename(distribution = key, observed = value) %>% bind_rows(random_simulations_2) # note we've repeated three times, time for a function # also note there are other distributions to try this on # and really, it may ne nice to simulate a few pulls of # the same size ggplot(random_simulations, aes(observed, fill = as.factor(distribution))) + geom_density(alpha = 0.2) + labs(title = "Simulate N(0,1)")

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