Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point. Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. This even applies to examples such as body heights used in textbooks to illustrate the normal distribution. RA Fisher's data of 1164 men yield a p value of a Chisquare goodness of fit of 0.13 for the normal, and of 0.48 for the log-normal distribution. Exceptions to these findings are measurements that can adopt negative values, like angles and Normal Distribution is defined as the probability distribution that tends to be symmetric about the mean; i.e., data near the mean occurs more as compared to the data far away from the mean. The two parameters of normal distribution are mean (μ) and standard deviation (σ). Hence, the notation of the normal distribution is. Fitted distribution line: Displays the probability distribution function for a particular distribution (e.g., normal, Weibull, etc.) that best fits your data. A histogram graphs your sample data. On the other hand, a fitted distribution line attempts to find the probability distribution function for a population that has the maximum likelihood Normal distributions are denser in the center and less dense in the tails. Normal distributions are defined by two parameters, the mean (μ μ) and the standard deviation (σ σ ). 68% 68 % of the area of a normal distribution is within one standard deviation of the mean. Normal distribution, which is also referred to as the Gaussian distribution, denotes a probability distribution which shows symmetry regarding the mean. It signifies that the data that is closer to the average or mean occurs more frequently as compared to the data that is at a distance from the mean. When represented in a graph, normal .

what is normal distribution in data science