Why is stats so hard

The paradigm that statistics uses is not the cause-effect but the probability. And the human being has problems dealing with probabilities. In essence, people have much less difficulty solving problems expressed in frequencies than the same identical problem presented as the probability. Facts, not probabilities. This is what we understand as human beings.

Ironically, it has also been observed that, as the statistical literature and university lectures usually use the percentages rather than frequencies, students tend to turn frequencies into probabilities in solving a problem. There are very few good teachers, that you would like to listen to four hours without getting tired. And there are even fewer good statistics teachers. IMHO, a teacher should talk about statistics in terms of faith, trust, love and philosophy.

Not just numbers. Table of Contents 1. Introduction 2. Is Statistics Hard 3. Why is statistics so hard? Summary 5. FAQs 6. External references. Introduction Is statistics hard? How hard is statistics? Is statistics easy? How to get better at statistics? Is Trigonometry hard? Is Geometry hard? Is Calculus hard? Many students think that statistics involve a lot of calculations due to the complex formulas.

This is not true. Sometimes, the teachers are to be blamed for making statistics complex. Let us go through some tips that can help you get better at statistics: Build a good base with the terms used frequently in statistics.

Statistics is not a spectator's sport. You need to practice, practice, practice by working out problems in the homework assignments. Make your own notes. Use the internet to your advantage. Summary With the right teaching and learning approach, statistics can be made easier. We have two systems of thought i subconscious reactions that are based on semi-probabilistic associations, and ii conscious thinking that likes to have one model of the world and is really bad at probability.

This is why we need to use mathematics and other explicit techniques to help us deal with probabilities. Statistics both needs this mathematics of probability and an understanding of what it means in the real world. If you present a reward enough times with the behaviour you want, the pigeon will learn to do it even when the original reward no longer happens.

This low-level learning is semi-probabilistic in the sense that if rewards are more common the learning is faster or if rewards and penalties both happen at different frequencies, then you get a level of trade-off in the learning. At a cognitive level one can think of strengths of association being built up with rewards strengthening them and penalties inhibiting them. This kind of learning is not quite a weighted sum of past experience: for example negative experiences typically count more than positive ones, and once a pattern is established it takes a lot to shift it.

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