Tips and tricks

Why do math people hate statistics?

Why do math people hate statistics?

Mathematicians hate statistics and machine learning because it works on problems mathematicians have no answer to. It works with finite state machines (i.e. computers), in comparison to mathematicians infinite state machines that do not exist in the real world. That’s why mathematicians hate it.

Why do some people struggle with statistics?

Why is statistics so hard? There are a lot of technical terms in statistics that may become overwhelming at times. It involves many mathematical concepts, so students who are not very good at maths may struggle. The formulas are also arithmetically complex, making them difficult to apply without errors.

Why is it worth to learn statistics?

Statistical knowledge helps you use the proper methods to collect the data, employ the correct analyses, and effectively present the results. Statistics is a crucial process behind how we make discoveries in science, make decisions based on data, and make predictions.

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Why did you choose statistics?

To summarize, the five reasons to study statistics are to be able to effectively conduct research, to be able to read and evaluate journal articles, to further develop critical thinking and analytic skills, to act a an informed consumer, and to know when you need to hire outside statistical help.

Why statistics is not yet considered as pure mathematics?

We use the singular is and not the plural are to emphasize that statistics is a field of study, not just a bunch of numbers. We use mathematical as an adjective because although statistics certainly makes use of much mathematics, it is a separate discipline and not a branch of mathematics.

Can statistics be misused?

That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.