## Time for action – analyzing random values

We will generate random values that mimic a normal distribution and analyze the generated data with statistical functions from the `scipy.stats` package.

1. Generate random values from a normal distribution using the `scipy.stats` package:

`generated = stats.norm.rvs(size=900)`
2. Fit the generated values to a normal distribution. This basically gives the mean and standard deviation of the dataset:

`print("Mean", "Std", stats.norm.fit(generated))`

The mean and standard deviation appear as follows:

```Mean Std (0.0071293257063200707, 0.95537708218972528)
```
3. Skewness tells us how skewed (asymmetric) a probability distribution is (see http://en.wikipedia.org/wiki/Skewness). Perform a skewness test. This test returns two values. The second value is the p-value—the probability that the skewness of the dataset does not correspond to a normal distribution.

### Note

Generally speaking, the p-value is the probability of an outcome different than what was expected given the null hypothesis...