{"id":6473,"date":"2019-09-16T19:08:33","date_gmt":"2019-09-17T02:08:33","guid":{"rendered":"https:\/\/careerkarma.com\/blog\/?p=6473"},"modified":"2020-05-23T00:21:58","modified_gmt":"2020-05-23T07:21:58","slug":"what-is-statistics-why-is-it-important","status":"publish","type":"post","link":"https:\/\/careerkarma.com\/blog\/what-is-statistics-why-is-it-important\/","title":{"rendered":"What Is Statistics (And Why Is It Important)?"},"content":{"rendered":"\n

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 TV without reading, hearing, or encountering people referencing one statistic or another. In some unfortunate circumstances the same statistic is tossed around by multiple people on opposing sides of an issue. 
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Statistics plays a role in debates ranging from gun control to immigration to climate change in politics, the effect of monetary policy in economics, the usefulness of drugs in psychology, and almost everywhere else. 
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For this reason, a working knowledge of statistics has become of paramount importance to anyone wanting to navigate the modern world.
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But for all that, such an understanding is distressingly rare. Indeed, it\u2019s fair to say that most people couldn\u2019t give a coherent definition of statistics, or distinguish it from related disciplines like probability and data science, or tell when statistics is being abused for the purpose of deception. 
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This piece will try to take a few steps towards alleviating this issue among Career Karma<\/a>\u2019s audience. We\u2019ll begin by trying to define statistics while separating it out from fields with which it\u2019s commonly confused. Then we\u2019ll enumerate the many reasons statistics is important, offer some comments on how to spot deception when statistics are being used, and finish with a guide to learning more about this important subject.
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It\u2019s easy to imagine that statistics is a dry subject to be relegated to dusty old books that only math nerds read. But it\u2019s actually exceptionally lively, with more schools of thought, heated debates, and long-standing rivalries than you\u2019ll find outside of Game of Thrones<\/em>. 
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So let\u2019s explore what statistics has to offer.  
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Defining Statistics<\/strong><\/h2>\n\n\n\n
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The concept of statistics can be surprisingly hard to nail down with certainty. <\/figcaption><\/figure>\n\n\n\n

There are a few different ways of defining statistics. According to the statistics department<\/a> at the University of California at Irvine, statistics is \u201c…the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data.\u201d
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Each part of the definition is important, so let\u2019s examine each in turn:
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As a field, statistics contains many different techniques for processing and understanding data, with new ones being developed all the time. If your knowledge of statistics comes entirely from one or two classes in college, it might be easy to miss how active statistics is. 
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You might imagine that collecting data would be relatively straightforward, but it can be unexpectedly tricky. Say that you want to study happiness, because you\u2019re interested in whether people are happier when they live in places with more sunshine. In order to do that you need to come up with a way of measuring happiness. If you want to go the easy route and just ask people on a survey, then you need to come up with some way of making sure people mean the same thing by \u2018happiness\u2019. If you don\u2019t, you\u2019ll wind up with misleading results. 
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When most people think about statistics, what they usually imagine is the \u2018analyzing data\u2019 part. It\u2019s true that a lot of the meat of statistics consists of activities like determining whether two groups of people are really<\/em> different from each other, and this is almost always done with some kind of statistical test.  
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All the data and fancy math in the world doesn\u2019t do much good if you don\u2019t understand what any of it means. Interpreting the results of statistical tests is almost as important as correctly running them in the first place. This is even more true when you are working with non-technical people who need statistical information to make good decisions in business or in life.
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There\u2019s an entire subfield of statistics which is concerned with visualizing results. I\u2019m constantly amazed at how difficult it can be to create charts and graphs which are accurate and don\u2019t lend themselves to being misinterpreted. 
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How Are Statistics and Probability Different? 
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Statistics and probability are commonly confused, but they\u2019re actually distinct subjects. Probability addresses the problem of determining how likely something is to occur. This is usually achieved by measuring the number of times an event could occur against the total number of conceivable outcomes (called the \u2018possibility space\u2019, if you want to impress your friends). 
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To illustrate: if you roll two dice, what is the probability of getting the same number on both dice? First, we enumerate the number of ways in which we can get the same number on two dice; I count six: one-one, two-two, three-three, four-four, five-five, six-six. Second, we figure out how big the possibility space is. With two six-sided dice there are six*six possibilities, for a total of 36.
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So the probability of getting the same number on two dice is 6\/36, or 1\/6th. 
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This is kind of a toy example, but it\u2019s still helpful. Think about it until it makes sense, then reread the definition of statistics provided above. It should be clear that while probability and statistics are related, they aren\u2019t the same thing.  
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How Are Statistics and Data Science Different?
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It\u2019s also pretty common to confuse statistics and data science, especially because data science is a famously slippery concept<\/a>. In broad terms, data science is a field which combines statistics, machine learning, computer science, and programming for the purpose of drawing actionable insights out of numerical information. It can be applied to problems like:
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