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).

When we use the term normal distribution in statistics, we usually mean a probability distribution. Good examples are the Normal distribution, the Binomial distribution, and the Uniform distribution. Alright. Let's start with a definition! A distribution in statistics is a function that shows the possible values for a variable and how often

Let's look into Normal Distribution in detail. Distribution is symmetrical in the middle, which is known as Mean(μ). In Normal Distribution, the values of Mean, Median, and Mode are equal. That means the distribution is also symmetrical at Median and Mode. There is a 1-2-3 Rule of Normal Distribution which follows the following three

Our data distribution could look like any of these curves. MLE tells us which curve has the highest likelihood of fitting our data. This is where estimating, or inferring, parameter comes in. As we know from statistics, the specific shape and location of our Gaussian distribution come from σ and μ respectively. In other words, μ and σ are
68-95-99.7 % Rule or Empirical Rule: We get to see this rule under the Normal or Gaussian distribution. whenever a data or random variable follows the normal distribution, then we can apply this rule to the data. So let's get to know a little bit about the Gaussian distribution. Gaussian distribution is symmetric distribution.
How to check data. A bell-shaped curve, also known as a normal distribution or Gaussian distribution, is a symmetrical probability distribution in statistics. It represents a graph where the data clusters around the mean, with the highest frequency in the center, and decreases gradually towards the tails. yexV7A.
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    • what is normal distribution in data science