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
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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