What is statistics (and why is it important)?

Statistics is one of those fields that everyone vaguely knows they should learn more about. You can hardly open a newspaper, read a nonfiction book, or turn on the television without reading, hearing, or seeing some statistic or another mentioned. In some unfortunate cases, the same statistic is thrown around by multiple people on different sides of an issue.

Statistics play a role in debates ranging from gun control to immigration to climate change in politics, the effects of monetary policy in the economy, the benefits of drugs in psychology, and just about everywhere else.

For this reason, knowledge of statistics is of utmost importance for anyone who wants to navigate the modern world.

Still, such understanding is shockingly rare. It can even be said that most people are unable to give a coherent definition of statistics, separating it from related disciplines such as probability and the Data science to differentiate or recognize when the statistics are being misused for the purpose of deception.

In this post, we will try to take some steps to mitigate this problem for Career Karma's target audience. First, we will try to define statistics and differentiate it from the fields with which it is often confused. Then we will list the many reasons why statistics are important, make some notes about how to recognize deception when using statistics, and conclude with a guide to learning more about this important topic.

It's easy to imagine that statistics is a dry subject, relegated to dusty old books that only math nerds read. In fact, it's an extraordinarily vibrant subject with more schools of thought, heated debates, and long-standing rivalries than you'll find outside of Game of Thrones.

So let’s explore what statistics has to offer.

Definition of statistics

There are different ways to define statistics. According to the Statistics Department at the University of California, Irvine, statistics is "...the science concerned with the development and study of methods for collecting, analyzing, interpreting, and presenting empirical data."

Each part of the definition is important, so let's examine each part in turn:

As a field, statistics encompasses many different techniques for processing and understanding data, and new ones are constantly being developed. If your knowledge of statistics comes solely from one or two lectures at the Universität comes from, you could easily miss how active statistics is.

You might think that collecting data is relatively easy, but it can turn out to be unexpectedly difficult. Let's say you want to study happiness because you're interested in whether people are happier when they live in places with more sunshine. To do this, you need to come up with something to measure happiness. If you want to take the easy route and simply ask people in a survey, you'll have to think of something to make sure people mean the same thing by "happiness." If you don't do that, you will get misleading results.

When most people think of statistics, they usually imagine the part that has to do with analyzing data. It's true that a large part of the content of statistics is determining whether two groups of people really differ from each other, and this is almost always done with some kind of statistical test.

All data and all sophisticated mathematics are of no use if you don't understand what they mean. Interpreting the results of statistical tests is almost as important as correctly performing the tests themselves. This is even more true when you work with non-technical people who need statistical information to make good decisions in business or life.

There is a whole subfield of statistics that deals with the visualization of results. I'm always amazed at how difficult it can be to create charts and graphs that are accurate and cannot be misinterpreted.

• Teaching computers to correctly diagnose patients using X-rays.
• Develop algorithms for trading stocks in the financial markets.
• Recommending books, movies, and even perfumes based on other things you like.

Data science relies heavily on statistics for its analytical methods, but they are not the same thing.

The importance of statistics

There are several reasons why everyone should have some understanding of statistics and how they work.

Statistics in politics

Statistics are used almost constantly by politicians and commentators discussing political activity. If a politician wants to raise the minimum wage, he or she will likely cite figures that show that the average wage does not cover the expenses necessary to live a comfortable life today. If someone else wants to stop the increase, they will probably talk about the statistical connection between minimum wage increases and unemployment.

Health statistics

Does eating red meat contribute to cancer? Does drinking red wine contribute to longevity? For any credible diet or exercise routine, there is a wealth of information and corresponding statistics to examine. It is often difficult to establish a clear connection between activity A and outcome B. For example, in the 20th century there were bitter debates about whether or not smoking caused cancer.

Statistics in Education

It is common knowledge that a good education is the key to a good future. But like everything else we've discussed, figuring out exactly what works in an educational setting can be difficult. If you use a new teaching strategy with a group of children and their test scores are better than another group, can we be sure that the strategy is causing the difference? Maybe it's just that we accidentally put the smarter kids in one group, and if we reshuffle the groups the effect will disappear.

Statistics in machine learning

Considering the fact that machinery Since we'll soon be pairing ourselves up with partners for dates, setting up a date, driving us to the restaurant and paying the bill, it's good to at least know how it's all going to work under the hood. Machine learning and artificial intelligence are no longer so mysterious when you know that they are usually based on statistics.

In my opinion, there is no getting around the fact that statistical knowledge is increasingly becoming a skill, like the ability to read or use the Internet: an essential skill without which life in the modern world becomes much more difficult.

A quick guide to not being lied to with statistics

I could build an entire writing career on this topic, so the advice in this section must necessarily be brief. Above all, I want to give you a sense of how statistics can be misused, whether intentionally or not.

Beware of the measures of central tendency:

You might think that the concept of "average" is pretty simple, but it can hide a lot of information. For example, there are at least two ways to accurately say that the average height in this group is 1.70 m. You could have a group of people who are almost all exactly this size. Or you have a group of people who are much smaller, with one person who is six feet tall. When you hear a claim like "Philosophy graduates earn more over their lifetime than people who studied other subjects," make sure it's not just because there are a few billionaires who studied philosophy.

Remember that causality is difficult to prove
Proving that a is the cause of b is notoriously difficult, but that doesn't stop people from structuring their arguments in a way that sort of sneaks in that claim. Just because two things are correlated doesn't mean one caused the other.

Sometimes the differences are just due to chance

As I mentioned in a previous section, it can be difficult to collect valid statistical data. This is partly because the world is very big and a lot of random things happen in it. Imagine you want to test a new drug to treat depression. The most obvious thing would be to put together two groups of depressed people and give the drug to only one group and then see whether the situation in this group develops better than in the group that does not receive the drug. One of the problems with this is that it is very easy to randomly select two groups that differ in subtle ways. For example, we might not care that a group contains more women than men, unless it turns out that men are more prone to depression than women. As with the previous bullet point, you need to remember that chance plays a big part in where people end up in life.

It is important to adopt a skeptical attitude when presented with statistical justification for an argument. Whatever the problem, it's up to you to assess the facts for yourself and pay attention to anything that doesn't look right.