The normal distribution is commonly associated with the 68-95-99.7 rule which you can see in the image above. 68% of the data is within 1 standard deviation (σ) of the mean (μ), 95% of the data is within 2 standard deviations (σ) of the mean (μ), and 99.7% of the data is within 3 standard deviations (σ) of the mean (μ). For Example, For a Normal Distribution, which is Symmetric, the value of Skewness equals 0 and that distribution is symmetrical. In general, Skewness will impact the relationship of mean, median, and mode in the described manner: Incorporating moments into your data science toolkit will enhance your ability to extract meaningful information Normal distribution is not the only "ideal" distribution that is to be achieved. Data that do not follow a normal distribution are called non-normal data. In certain cases, normal distribution is not possible especially when large samples size is not possible. In other cases, the distribution can be skewed to the left or right depending on Normal distribution. Normal distribution, is, well, normal because it describes many of the natural phenomenon out there: blood pressure, measurement error, IQ scores, etc. The mathematical formula for normal distribution is as follows, where μ (read: miu) is the mean and σ (read: sigma) is the deviation of the observations. These normality tests compare the distribution of the data to a normal distribution in order to assess whether observations show an important deviation from normality. The two most common normality tests are Shapiro-Wilk's test and Kolmogorov-Smirnov test. Both tests have the same hypotheses, that is: \(H_0\): the data follow a normal Normal (Gaussian) Distribution . Being a continuous distribution, the normal distribution is most commonly used in data science. A very common process of our day to day life belongs to this distribution- income distribution, average employees report, average weight of a population, etc. The formula for normal distribution; Where μ = Mean value, The normal distribution is essential when it comes to statistics. Not only does it approximate a wide variety of variables, but decisions based on its insights have a great track record. If this is your first time hearing the term 'distribution', don't worry. 20WN.

what is normal distribution in data science