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?

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 the
quantile() function in Julia’s Distributions package.

If you don’t have that package installed, first run using Pkg and then
Pkg.add( "Distributions" ) from within Julia.

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

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usingDistributionsalpha=0.05# Replace with your alpha valuestandard_normal=Normal(0,1)quantile(standard_normal,alpha)# Critical value for a left-tailed test

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-1.6448536269514724

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quantile(standard_normal,1-alpha)# Critical value for a right-tailed test

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1.6448536269514717

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quantile(standard_normal,alpha/2)# Critical value for a two-tailed test

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-1.9599639845400592

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 the cdf() function
in Julia’s Distributions package.
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 statisticcdf(standard_normal,test_statistic)# p-value for a left-tailed test

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0.9962074376523146

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1-cdf(standard_normal,test_statistic)# p-value for a right-tailed test

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0.0037925623476854353

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2*(1-cdf(standard_normal,test_statistic))# p-value for a two-tailed test