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### 5 Best Basic Statistics Concepts All Data Scientists Need to Know

**Statistical Features**

- When the box plot is short it implies that much of your data points are similar, since there are many values in a small range
- When the box plot is tall it implies that much of your data points are quite different, since the values are spread over a wide range
- If the median value is closer to the bottom then we know that most of the data have lower values. If the median value is closer to the top then we know that most of the data have higher values. Basically, if the median line is not in the middle of the box then it is an indication of skewed data.
- Are the whiskers very long? That means your data has a high standard deviation and variance i.e the values are spread out and highly varying. If you have long whiskers on one side of the box but not the other, then your data may be highly varying only in one direction.

- A Uniform Distribution is the most basic of the 3 we show here. It has a single value which only occurs in a certain range while anything outside that range is just 0. It's very much an Ć¢??on or offĆ¢?? distribution. We can also think of it as an indication of a categorical variable with 2 categories: 0 or the value. Your categorical variable might have multiple values other than 0 but we can still visualize it in the same was as a piecewise function of multiple uniform distributions.
- A Normal Distribution, commonly referred to as a Gaussian Distribution, is specifically defined by its mean and standard deviation. The mean value shifts the distribution spatially and the standard deviation controls the spread. The import distinction from other distributions (e.g poisson) is that the standard deviation is the same in all directions. Thus with a Gaussian distribution, we know the average value of our dataset, as well as the spread of the data i.e, is it spread over a wide range or is it highly concentrated around a few values.
- A Poisson Distribution is similar to the Normal but with an added factor of skewness. With a low value for the skewness, a Poisson distribution will have relatively uniform spread in all directions just like the Normal. But when the skewness value is high in magnitude then the spread of our data will be different in different directions; in one direction it will be very spread and in the other, it will be highly concentrated.