The normal distribution has two parameters (two numerical descriptive measures), the mean (μ) and the standard deviation (σ). The normal distribution, which is continuous, is the most important of all the probability distributions. Its graph is bell-shaped. This bell-shaped curve is used in almost all disciplines. The above distribution is only valid if, X is approximately normal or sample size n is large, and,; the data (population) standard deviation σ is known. If X is normal, then X̅ is also normally distributed regardless of the sample size n.Central Limit Theorem tells us that even if X is not normal, if the sample size is large enough (usually greater than 30), then X̅'s distribution is How to plot Gaussian distribution in Python. We have libraries like Numpy, scipy, and matplotlib to help us plot an ideal normal curve. import numpy as np import scipy as sp from scipy import stats import matplotlib.pyplot as plt ## generate the data and plot it for an ideal normal curve ## x-axis for the plot x_data = np.arange(-5, 5, 0.001 What is Normal Distribution? Data that is Normally Distributed ; HOW TO FIND A CAREER IN DATA SCIENCE: The Expert Guide to become a 6 Figure Data Scientist in 12 months. The -2.82 is a theoretical Z-score, a.k.a. the value below which we expect 0.237% of our observations to lie on a normal distribution. Now let's calculate the Z-score of our actual data. The 2 outlier dots represent disastrous monthly returns of -20.4% (2008 Financial Crisis) and -22.5% (this past month). The shape of the distribution doesn't change. Think about how a scale model of a building has the same proportions as the original, just smaller. That's why we say it is drawn to scale. The range is often set at 0 to 1. Standardize generally means changing the values so that the distribution's standard deviation equals one. Scaling is .

what is normal distribution in data science