The Normal Distribution is really important in statistics. Here’s why:
Central Limit Theorem: This idea says that when you add up a lot of unrelated random things, the result will look like a normal distribution. This is true no matter how the original things were spread out.
Probability Calculations: The normal distribution is based on two main numbers: the mean (average) and standard deviation (how spread out the data is). About 68% of the data is within one standard deviation from the mean. Around 95% falls within two standard deviations, and about 99.7% is within three standard deviations.
Applications: The normal distribution is used a lot in tests, data analysis, and making sure products meet quality standards. It’s useful because we see it a lot in real life and it can be calculated easily.
The Normal Distribution is really important in statistics. Here’s why:
Central Limit Theorem: This idea says that when you add up a lot of unrelated random things, the result will look like a normal distribution. This is true no matter how the original things were spread out.
Probability Calculations: The normal distribution is based on two main numbers: the mean (average) and standard deviation (how spread out the data is). About 68% of the data is within one standard deviation from the mean. Around 95% falls within two standard deviations, and about 99.7% is within three standard deviations.
Applications: The normal distribution is used a lot in tests, data analysis, and making sure products meet quality standards. It’s useful because we see it a lot in real life and it can be calculated easily.