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## How do you know which test for normality?

Power is the most frequent measure of the value of a test for normality—the ability to detect whether a sample comes from a non-normal distribution (11). Some researchers recommend the Shapiro-Wilk test as the best choice for testing the normality of data (11).

### How do I know if my normality is normal?

value of the Shapiro-Wilk Test is greater than 0.05, the data is normal. If it is below 0.05, the data significantly deviate from a normal distribution. If you need to use skewness and kurtosis values to determine normality, rather the Shapiro-Wilk test, you will find these in our enhanced testing for normality guide.

**Why do we need to test for normality?**

For the continuous data, test of the normality is an important step for deciding the measures of central tendency and statistical methods for data analysis. When our data follow normal distribution, parametric tests otherwise nonparametric methods are used to compare the groups.

**Do you need to do a normality test?**

However, normality tests are not the way for us to do this. Howeve r, in large samples (n > 30) which most of our work as data scientists is based upon the Central Limit Theorem usually applies and we need not worry about the normality of our data.

## Is it possible to detect deviations from normality?

In small samples these tests are underpowered to detect quite major deviations from normality which can be easily detected through graphical methods. In larger samples these tests will detect even extremely minor deviations from theoretical normality that are not of practical concern.

### When to use normality assumption in statistical analysis?

The normality assumption also needs to be considered for validation of data presented in the literature as it shows whether correct statistical tests have been used.

**Is the two sample t-test assumes normality?**

So, as constructed, the two-sample t-test assumes normality of the variable X in the two groups. On the face of it then, we would worry if, upon inspection of our data, say using histograms, we were to find that our data looked non-normal.