{"id":174,"date":"2020-02-11T10:26:18","date_gmt":"2020-02-11T10:26:18","guid":{"rendered":"http:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/tessa-wilkie\/?p=174"},"modified":"2020-04-28T15:23:48","modified_gmt":"2020-04-28T15:23:48","slug":"why-statistics","status":"publish","type":"post","link":"https:\/\/www.lancaster.ac.uk\/stor-i-student-sites\/tessa-wilkie\/2020\/02\/11\/why-statistics\/","title":{"rendered":"Why statistics?"},"content":{"rendered":"\n

This a personal post on what I think statistics is, why I\nwas drawn to study it, and why it\u2019s basically a super awesome cool subject\neveryone should know more about. <\/p>\n\n\n\n

Many people appear to regard the subject with suspicion, feeling that statistics are more often used to bamboozle than to enlighten. <\/p>\n\n\n\n

When, in my 20s, I started studying Applied Statistics in\nthe evenings I got a lot of puzzled looks from friends and colleagues. What on\nearth did I want to do that for? What was the point? <\/p>\n\n\n\n

To me this seemed odd. <\/p>\n\n\n\n

As a journalist you are always trying to find out what is\ngoing on, and what it might mean. In my History degree I looked to try to find\nout what went on in the past and what it might mean. <\/p>\n\n\n\n

These questions are very similar to those that statisticians\nask. Only, statisticians have an additional tool to help them \u2014 and it\u2019s a big\none: maths. <\/p>\n\n\n\n

Mathematical frameworks help us understand what conclusions\nwe can draw from data, and how confident we can be in them. They give us tools\nto deal intelligently with what we do not \u2014 or cannot \u2014 know. <\/p>\n\n\n\n

And this is great. Because of course, in the real world, we\nnever get all the information we need. We are always having to piece together a\npicture based on what we can see, and we need steer on what we cannot see and\nhow important that might be. <\/p>\n\n\n\n

For example: I might have a disagreement with my dog, Markus, about his biscuits. He might insist that Brand A\u2019s dog biscuits are bigger than Brand B\u2019s, and therefore he should be bought Brand A\u2019s. <\/p>\n\n\n\n

As he\u2019s a scientifically minded dog, he would allow me to take\na random sample of each to weigh. <\/p>\n\n\n\n

What if, based on the sample, Brand A\u2019s biscuits are slightly\nlarger than Brand B\u2019s? Are Brand A\u2019s biscuits bigger, or could this be a fluke?\n<\/p>\n\n\n\n

Well, there are statistical tests to decide whether there is actually enough evidence to accept Markus\u2019s claim. <\/p>\n\n\n\n

\"\"
Markus protesting about biscuits<\/figcaption><\/figure><\/div>\n\n\n\n

There are also well defined frameworks to assess the probability\nthat you will wrongly accept the claim by chance (that is, you select a random\nsample that happens to be of unusually large biscuits from Brand A, when in\nfact Brand A\u2019s biscuits are not bigger than Brand B\u2019s). <\/p>\n\n\n\n

Of course, advanced statistical methods deal with much more\ndifficult and nuanced situations than my dog biscuit example. <\/p>\n\n\n\n

Statistics has not advanced to the point where we can guarantee that we are right about everything all the time*. But that’s not a reason to dismiss it. <\/p>\n\n\n\n

Statistical methods, if used properly, bring us a vast amount of insight into problems. Why wouldn’t you want to know about that? <\/p>\n\n\n\n

\n\n\n\n

*On the subject of things I’ve not always been right about: I was going to put something in this post about Benjamin Disraeli\u2019s famous \u201cLies, damned lies and statistics\u201d quote, only to discover that we don\u2019t actually know who coined that one.[1]<\/a>
<\/p>\n\n\n\n

\n\n\n\n

[1]<\/a> https:\/\/www.york.ac.uk\/depts\/maths\/histstat\/lies.htm<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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