The distribution of data means the way the data gets spread out. This article talks about some essential concepts of the normal distribution:

- How to measure normality
- Ways to transform a dataset to fit the normal class distribution
- How to use the normal distribution to showcase naturally distributed phenomena and provide statistical insights

**Let’s get started!**

Suppose you belong to the field of statistics. In that case, you know how vital data distribution is because we always sample from a population where you have no idea about full distribution. As a result, the distribution of our sample might limit the statistical techniques available to us.

Looking at the normal distribution, it is a frequently perceived continuous probability distribution.

When a database meets the normal distribution, you can employ other techniques to explore the data more.

- Knowledge about the percentage of data in each standard deviation
- Linear least-squares regression
- Inference based on the sample mean

In some cases, it can be beneficial to change a skewed dataset to observe the normal distribution. It will be more relevant when your data is usually distributed for some distortion.

Here are the basic features of the normal distribution:

- Symmetric bell shape
- Equal Mean and median at the center of the distribution
- ≈68% of the comedown within 1 standard deviation of the mean
- ≈95% of the data come down within 2 deviations of the mean
- ≈99.7% of the data falls between 3 standard deviations of the mean

M.W. Toews via Wikipedia

Important terms you need to know as a general overview of the normal distribution:

**Normal Distribution:**It is a symmetric probability distribution frequently used to represent real-valued random variables. Also called the bell-curved or Gaussian distribution.**Standard Deviation:**It measures the amount of variation or dispersion of a set of values. It is also calculated as the square root of variance.**Variance:**It is the distance from the mean of each data point

**Ways to Use Normal Distribution**

If the dataset you have does not conform to the normal distribution, you could apply these tips.

**Collect more data:**Even a tiny sample size lacking quality could distort your customarily distributed dataset. As a solution, collecting more data is the key.**Reduce sources of variance:**Reducing the outliers can help with the normal distribution of data.**Apply a power transform:**You can choose to apply the Box-Cox method for skewed data, which refers to…

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