Hi everyone,

I'm currently working on a data set of heavy metals and I need to perform some transformation in order to make the data normal (follow a normal distribution). You can see here the distribution of the Ni data after deleting some outliers :

So I compute the Shapiro-wilk test on my initial data to see if it was necessary:

```
shapiro.test(clean.donnees.Ni$Ni)
```

and I get this as result :

```
Shapiro-Wilk normality test
data: clean.donnees.Ni$Ni
W = 0.97966, p-value < 2.2e-16
```

This seems rather strange to me because the distribution almost looks like a true normal distribution.

But I was not a the end of surprises !

I still made a BoxCox transformation of my data using the `AID`

package :

```
Cdbx.Nii <- boxcoxnc(clean.donnees.Ni$Ni, verbose = FALSE) # Find best alpha
clean.donnees.Ni[!is.na(Ni), Cdbx.Nii := Cdbx.Nii$tf.data] # Create column with transformed data
alpha <- Cdbx.Nii$lambda.hat
```

and then I tried the Shapiro-Wilk test on my transformed data

```
shapiro.test(Cdbx.Nii$tf.data)
```

and this is my results :

```
Shapiro-Wilk normality test
data: Cdbx.Nii$tf.data
W = 0.99377, p-value = 9.967e-10
```

With this distribution of data, I really don't understand how the Shapiro-Wilk test can't be successful.

How you can help me to resolve this !