The normal distribution is really helpful in many situations, but there are times when it doesn’t work so well. Let’s look at some of these situations:
Skewed Distributions: If your data is not evenly spread out, the normal distribution isn’t a good choice. For example, in income data, there are a few people who make a lot more money than everyone else. This makes the data right-skewed. In these cases, using a log-normal distribution can give you better results.
Categorical Data: When you work with categorical data, like yes/no answers or types of animals, the normal distribution doesn’t fit. Instead, you might want to use the binomial distribution to analyze this kind of information.
Small Sample Sizes: If you have a small group of data, the normal distribution might not work well. In these cases, it’s often better to use the exact binomial or Poisson distributions, depending on your situation.
Bimodal Distributions: If your data has two different peaks (bimodal), the normal distribution can’t show that because it only has one peak or bell curve. In these cases, you would need a mixture model that can handle both peaks.
In short, knowing when to use something other than the normal distribution can make your analysis more accurate and useful!
The normal distribution is really helpful in many situations, but there are times when it doesn’t work so well. Let’s look at some of these situations:
Skewed Distributions: If your data is not evenly spread out, the normal distribution isn’t a good choice. For example, in income data, there are a few people who make a lot more money than everyone else. This makes the data right-skewed. In these cases, using a log-normal distribution can give you better results.
Categorical Data: When you work with categorical data, like yes/no answers or types of animals, the normal distribution doesn’t fit. Instead, you might want to use the binomial distribution to analyze this kind of information.
Small Sample Sizes: If you have a small group of data, the normal distribution might not work well. In these cases, it’s often better to use the exact binomial or Poisson distributions, depending on your situation.
Bimodal Distributions: If your data has two different peaks (bimodal), the normal distribution can’t show that because it only has one peak or bell curve. In these cases, you would need a mixture model that can handle both peaks.
In short, knowing when to use something other than the normal distribution can make your analysis more accurate and useful!