# Left-tailed test (example) ¶

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This is also a basic example, this time for a left-tailed test. We found what's the statistical test function, but this was an easy one. Let's say we generated a sample, following a Normal distribution $N(\mu, 1)$.

# ### ### #
# NOTE
# You are not supposed to know that mean=1.2
# ### ### #
x <- rnorm(n = 100, mean = 1.2,  sd = 1)


Note that $x \sim N(\mu, \frac{\sigma^2}{n})$ giving us $x \sim N(\mu, \frac{1^2}{100})$ here (sorry for the small $x$ but that's the variable name). Check multivariate normal distribution if you are looking for the formula. A multivariate normal distribution is a name given to a vector of normally distributed variables.

## Hypothesis ¶

We are making the left-tailed test under the two hypothesis

• $H_0: \mu = 1.2$
• $H_1: \mu \lt 1.0$

And $\alpha=0.05$.

## Determining W ¶

We are picking $T(x)=mean(x)$ as the test statistic. We know from the probabilities course that for a normal distribution we have

@ \alpha = \mathbb{P}(X \le c) = F_X^{-1}(\alpha) @ @ F_X^{-1}(\alpha) = \mu + \sigma * \phi^{-1}(\alpha) @ @ \phi^{-1}(0.05) = -\phi(1-0.05) = -\phi(0.95) = -1.645 @

We are replacing with $\mu=1.2$ ($H_0$) and $\sigma=\frac{1}{\sqrt{100}}$ (given at the start).

@ c = 1.2 + \frac{1}{\sqrt{100}} * -1.645 = 1.0355 @

## Result ¶

We got $W = \{x; T(x) \lt 1.0355\}$. It means that $W$ is defined as every mean below $1.0355$, since $T(x)$ is the mean here. If the mean ($T(X)$) is within $W$ then at a significance level of $\alpha=0.05$, we are rejecting $H_0$.

mean(x)
[1] 1.046398


For MY SAMPLE $x$, the mean is not within $W$, so we do not reject $H_0$ (remember: we do not write we "accept"). Here is some code that will give the same result for anyone running it

# reproductible
set.seed(0)
x <- rnorm(100, 1.2, 1)
mean(x)
# 1.222668