Why statistics matters

Statistics as a part of science

Science creates knowledge. This knowledge production often follows certain principles. Being a statistician, with all the experience I had the privilege to perceive to date, I recognize this as one of the largest problems of statistics. Many people, especially young people, feel a strong urge towards freedom. I think this is wonderful. Radical thinking moves us all forward. However, through this urge for freedom already existing knowledge is often not valued, as the radical thought for the new is stronger than the appreciaten of the already established. This is in my experience the first grave danger statistics face. People reject the principles of statistics out of the urge to be radical. I say, be radical, but also stand on the shoulders of giants. The second grave danger of statistics is numbers per se. Most people are not fond of numbers. Some are even afraid. Fear is the worst advisor. Numbers are nothing to be afraid of, and likewise is statistics nothing to be afraid of. Yet learning statistics is no short task, and this is the third obstacle I see. Learning a scientific method, any scientific method, takes time. It is like learning an instrument, or learning martial arts. Learning a method is bot a mere internalising of knowledge, but instead learning a method is about gaining experience. Be patient. Take the time to build this experience. Within this Wiki, I give you a basic introduction how statistics can help you to create knowledge, and how you can build this experience best.

Occam's razor

"Entities should not be multiplied without necessity."

The Franciscan friar William of Occam almost single-handedly came up with one of the most fundamental principles to date in science. He basically concluded, that "everything should be as simple as complex as necessary, but as simple as possible." Being a principle, it is suggested that this thought extends to all. While in his time it was rooted in philosophy or more specifically in logic, Occam's razor turned out to propel many scientific fields later on, such as physics, mathematics, biology, theology, mathematics and much more. It is remarkably how purely rooted on theoretical consideration this principle generated the foundation for the scientific method, which would surface centuries later out of it. It also poses one of the main building blocks of modern statistics as William of Occam came up with the principle of parsimony. While this is well known in science today, we are up until today busy discussing whether things are simple or complex. Much of the scientific debate up until is basically a pendulum swing between these two extremes, with some people oversimplifying things, while others basically say that everything is so complex we may never understand it. Occam's razor concludes that the truth is in between.

Example:

How to make a Curry.

I love Curry. Having had the privilege to work in Nepal for sixth months, I had the opportunity to enjoy some good curry. How does one make a good curry? I think this is an ideal case of Occam's razor. You need quite some ingredients, Chillies, Ginger, Garlic, Onions, some vegetables of your choice, coconut milk, etc. Do you need hundreds of things? Probably not. An ideal curry contains as much ingredients as one needs, but not more.

You have been Sherlocked

Many people are familiar with the fantastic Sherlock Holmes. His creator, Sir Arthur Canon Doyle was obviously a great admirer of Occam's razor. Hence, it surfaces a lot in Sherlock, but can be also found in lots of other characters from fiction (Contact, House, Star Trek).

The scientific method

The scientific method was a true revolution, since it enabled science to test hypothesis through observation. Before, science was vastly dominated by theorizing -that is developing theories- yet testing theories proved to be more difficult. While people or scientists tended to observe since the dawn of humans, making such observations in a systematic way opened a new world in science. All observations are normative, as they are made by people. This means that observations are constructs, where people tend to see things through a specific "lens". A good example of such a specific normative perspective is the number zero, which was kind of around for a long time, but only recognised as such in India and Arabia in the 8th-9th century (0). For us the 0 seems almost as if it was always there, but in the antique world it was far from unclear whether the 0 is an actual number. This illustrates how normative perspectives change and evolve, although not everybody may be aware of such radical developments as Arabic numbers.

A very short history of statistics

Building on Occam's razor and the scientific method, a new mode of science emerged. Rigorous observation and the testing of hypotheses became one important building block of our civilisation. One important foundation of statistics was probabilistic theory, which kind of hit it off during the Enlightenment. Probability was important as it allowed two differentiate between things happening by chance, or following underlying principles that can be calculated by probability. Of course probability does not imply that it can be understand why something is not happening by chance, but it is a starting point to get out of a world that is not understood. Statistics or more importantly probability were however not only an important scholar development during the enlightenment, they also became a necessity. Following the Peace of Westphalia, nations were formed at a previously unknown extend, effectively ending the European religious wars. Why is that relevant, you wonder? States need governance, and governance builds at least partly on numbers. Statistics enabled states to get information on demographics and economic development. Double bookkeeping contributed as well. Numbers became a central element of control of sovereigns over their nations. People started showing of graphs and bar charts to show off their development, something we have not quite recovered from ever since. Statisticians were increasingly in demand to account for data, find relations between observations, and basically to find meaning in an increasing plethora of information. The setup and calculation of experiments created another milestone between the two world wars, effectively propelling modern agriculture, psychology, clinical trials and many other branches of science. Hence, statistics became a launchpad for much of the exponential development we observe up until today. While correlation and the Analysis of Variance are a bit like the daily bread and butter of statistics, the research focus of the inventor of the ANOVA -Ronald Fisher- on Eugenics is a testimony that statistics can also be misused or used to basically create morally questionable or repugnant assumptions. Despite these drawbacks, statistics co-evolved with the rise of computers, and became a standard approach in the quantitative branch of science. With computers, long standing theories such as Bayes Theorem could be tested, and many new statistical methods got developed. Development of statistics led also to new developments in science, as for instance multivariate statistics paved the road to look at messy datasets, and machine learning revolutionised the way we approach information in the age of computers.

Key concepts of statistics

Models as simplifications

Statistics if about simplification of complex realities, or also about finding patterns in more or less complex data. Such simplifications are often called models. According to the statistician Box "..all models are wrong, but some models are useful. However, the approximate nature of the model must always be borne in mind." This famous quotation (Box and Draper 1987, page 424) is often misunderstood. Box talked about specific -in this case polynomial- models. These represent approximations or simplifications, and are hence inertly all wrong. Still, some of these specific models can be useful. With this, Box basically highlights the previously known principle of parsimony. The quotation if however often used to reject models altogether, which is simply a mistake. There is another famous quote that is associated to Winston Churchill “I only believe in statistics that I doctored myself”. Most likely this sentence was never said by Churchill, but was associated to him by Goebbels to discredit him. This showcases that the discussion about the approximation of truth through statistics is a controversial one. Never forget that statistics can generate approximations to truths, but I would argue it cannot generate fundamental truths. Is that a problem? NO! In most cases approximations will do. In other words, it is quite easy to prove something as a fact, but it is much harder to prove that something is not a fact. This is what any good conspiracy theory is build upon. Try to prove that flying saucers do not exist. Yet if one would land in front of you, its quite evident that there are flying saucers. This debate about truths has preoccupied philosophy for quite some time now, and we will not bother ourselves with it here. If you look for generalisable truths, I advise to go to philosophy. But if you look for more tangible knowledge, you are in the right place. Statistics is one powerful method to approximate truths.

Samples

If we would want to understand everything through statistics, we would need to sample everything. Clearly, this is not possible. Therefore, statistics work with samples. Samples allow to just look at a part of a whole, hence allowing to find patterns that may represent a whole. Take an example of leave size in a forest that consists of Beech trees. You clearly do not need to measure the leaves of all trees. However, you sample should be representative. Hence you would want to sample maybe small trees and large trees, trees at the edge of the forest, but also in the center. This is why in statistics people tend to take a random sample. This is quite important, since otherwise your analysis may be flawed. Much in statistics is dealing with sample designs that enable a representative samples, and there are also many analytics approaches that try to identify whether samples are flawed, and what could be done if this is the case. Proper sampling is central in statistics. Therefore, it is advisable to think early within a study about your sample design.

Analysis

Analysing data takes experience. While computers and modern software triggered a revolution, much can go wrong. There is no need to worry, as this can be avoided through hard work. You have to practise to get good at statistical analysis. The most important statistical test can be learned in a few weeks, but it takes time to build experience, enabling you to know when to use which test, and also how to sample data to enable which analysis. Also, statistical analysis is made in specific software tools such as R, SPSS or Stata, or even programming languages such as Python or C++. Learning to apply these software tools and languages takes time. If you are versatile in statistical analysis, you are rewarded, since many people will need your expertise. There is more data than experienced statistical analysts in the world. But more importantly, there are more open questions than answers in the world, and some of these answers can be generated by statistical analysis.