It will make you understand that there is more to data than just the mean, and will make you a more credible and precise presenter, in case you should ever present data yourself. In your everyday life, this will help you in better interpreting data and will make you more sceptical, when you are having data presented to you. Suddenly we realize that the mean value is quite a poor indication for the data in this set, since it is very sensitive to extreme values. For group B, we changed one value in the dataset to a much bigger number than in group A: instantly we can see how the mean increased- by a lot, whereas the median stayed equal. We can see that group A has a much lower mean close to the median value. 50% of our points are above, and 50% points the median value.Īs an example, let’s look at the below datasets A and B of people’s salaries:Įxample of two Datasets with Mean & Median Values. The mean is the average over all data points - the median splits the data in half i. Nevertheless, the mean can hide information on the data, which will only become visible to us by also looking at the median. People tend to use the mean more frequently than the median. Understanding the difference between the mean and the median is also something that is usually undervalued. It will make you more capable of distinguishing good arguments from false ones, and will help you in formulating valid and correct arguments to your peers. In your everyday life, this will help you understand situations and analyze claims better. They are related, because good weather causes more people to eat ice cream at the beach and go for a swim in the sea, which in turn, increases the likelihood of a shark attack. We often say “correlation does not imply causation”.Ī simple example might be: we generally observe that ice cream sales and the number of shark attacks increase when the weather is warm and sunny. Causation means one thing causes another, whereas correlation is just a simple relationship between two events that relate to each other. You will get an understanding that a correlation is not always equal to causation, and that you cannot just make assumptions about certain events, just because there is a correlation between them. Doing a data science class will give you a basic understanding of statistics, and will generally help you understand descriptions, situations, and implications better. A lot of popular methods used in data science rely in their core on classic statistical models. Statistics is a major building block of data science. Why? Reason 1 | It gives you an understanding of statistics I suggest everyone to take a class in data science, no matter what industry you are working in. Knowledge of data science is not just useful for those developing models, processing the data, analyzing the statistics, and coding all day. … and time will tell even more reasons why data science as a field is, and can be so important.
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