# How to find critical values and p-values from the normal distribution (in R)

## Task

Some statistical techniques require computing critical values or $p$-values from the normal distribution. For example, we need to do this when constructing a confidence interval or conducting a hypothesis test. How do we compute such values?

Related tasks:

## Solution

If we choose a value $0 \le \alpha \le 1$ as our Type 1 error rate,
then we can find the critical value from the normal distribution using R’s
`qnorm()`

function. The code below shows how to do this for left-tailed,
right-tailed, and two-tailed hypothesis tests.

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alpha <- 0.05 # Replace with your alpha value
qnorm(p = alpha, lower.tail = TRUE) # Critical value for a left-tailed test
qnorm(p = alpha, lower.tail = FALSE) # Critical value for a right-tailed test
qnorm(p = alpha/2, lower.tail = FALSE) # Critical value for a two-tailed test

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[1] -1.644854
[1] 1.644854
[1] 1.959964

We can also compute $p$-values from the normal distribution to compare to a test statistic. As an example, we’ll use a test statistic of 2.67, but you can substitute your test statistic’s value instead.

We can find the $p$-value for this test statistic using R’s `pnorm()`

function.
Again, we show code for left-tailed, right-tailed, and two-tailed tests.

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test_statistic <- 2.67 # Replace with your test statistic
pnorm(test_statistic, lower.tail = TRUE) # p-value for a left-tailed test
pnorm(test_statistic, lower.tail = FALSE) # p-value for a right-tailed test
2*pnorm(test_statistic, lower.tail = FALSE) # p-value for a two-tailed test

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[1] 0.9962074
[1] 0.003792562
[1] 0.007585125

Content last modified on 24 July 2023.

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Contributed by Elizabeth Czarniak (CZARNIA_ELIZ@bentley.edu)