Statistical Significance with the help of Python

Statistical Significance with the help of Python

This example will use Python to show how to represent statistical significance in your code/ jupyter notebook. It is recommended you understand some Python basics. We will also use the following Python libraries NumPy, Matplotlib, and Pandas.

“Using ads create more revenue for our product”. “ The weight loss pill caused greater weight loss than those who took a placebo.” “Battery ‘A’ last ten times longer than its competitor.” These types of statements appear often. When looking at data it can be tempting to make a quick assumption that a variable resulted in a certain result. Being too hasty with these conclusions can cause a lot of problems. In Data science being able to determine if a result is due to chance can help in selecting a model as well as a way to check for sampling errors.

One of the ways to build confidence that a result isn’t due purely to chance is by determining statistical significance. _Let’s take a look at what statistical significance is how to determine it by using the _p-value.

This example will use Python to show how to represent statistical significance in your code/ jupyter notebook. It is recommended you understand some Python basics. We will also use the following Python libraries NumPyMatplotlib, and Pandas. If you aren’t familiar with any of them I recommend you use the links provided.

statistics python code data-science

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