Data Science; Software Education & Teaching; The normal distribution is a distribution of data that is bell-shaped and symmetrical. It is also called the normal curve. It occurs in nature in The most frequently occurring type of data and probability distribution is the normal distribution. A symmetrical bell-shaped curve defines it. However, under the influence of significant causes, the normal distribution too can get distorted. This distortion can be calculated using skewness and kurtosis. Definition: The Normal Distribution can be defined by the probability density function for a continuous random variable in a system. If f (x) is the probability density function, X is the random variable; then it defines a function that is integrated between the range or interval (x to x + dx). Thus, the probability of random variable X is The Empirical Rule If X is a random variable and has a normal distribution with mean µ and standard deviation σ, then the Empirical Rule states the following:. About 68% of the x values lie between -1σ and +1σ of the mean µ (within one standard deviation of the mean).; About 95% of the x values lie between -2σ and +2σ of the mean µ (within two standard deviations of the mean). 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 • The normal distribution can be used to make better prediction of the number of failures that will occur in the long term. • In our case, the Z-table predicts the area under the curve to be 0.6% for a Z-value of 2.5. • This is a better prediction than the 0% assumed earlier. - Normal Distribution .

what is normal distribution in data science