Do you need statistics to understand your data?

Do we actually need statistics to understand our data? Surely it would be easier to just simply look at the data as a whole and assume common trends and be done with it? It’s just the same as doing a jig-saw puzzle, you get half way through and you already know what the pictures going to be. But with each statistic another piece is added to the puzzle and sure enough, we finally get the full picture. From there we can appreciate the data for what its worth, be it evidence for a genetic predisposition for aggression*, or simply a cute picture of a dog smiling.

By breaking down data to individual statistics, we can understand and interpret data in more in-depth and useful ways. Rather than just saying there is a correlation between delinquent behaviour and poverty**, we can look at the presented data and measure the magnitude of the effect. Get right down to the crux of the issue, and from there can formulate theories and strategies in how to mediate this delinquent behaviour. This could be anything from social support such as group meetings, to individual therapy and counselling addressing the behaviour directly.

 

Not only this, but statistics enable your data to be presented and evaluated in more exciting and interesting ways. (I know, who thought this was possible) But with the magic of stats, endless reams of data compiled in tables of apparent gobbledegoop, can be transformed into simple and easy to understand graphical representations. From here we can simply look at the graph and say “It appears there is a positive correlation between squeaky voices and helium intake” Quite simply, the possibilities of data analysis becomes near infinite with the joy that is statistics.

So to conclude, statistics are not simply there to confuse and scare us, but quite the opposite. They are a useful tool into identifying the important bits of data that may have previously been overlooked. And while they may seem a tad dull and boring at times, remember that without statistics you would never know that 100 people a year choke to death on ball-point pens.***

 

 

*http://psycnet.apa.org/psycinfo/1991-12819-001

 

**https://www.ncjrs.gov/App/Publications/abstract.aspx?ID=160998

 

***http://www.allfunandgames.ca/facts/statistics.shtml

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11 thoughts on “Do you need statistics to understand your data?

  1. I really enjoyed your blog you explained very clearly the positives of having a good statistical background and why that is important. I agree completely with your blog the only thing i would suggest adding would be to talk about how companies can rig statistics to make there product sound better.

  2. great blog you bring up key points about how stats helps us in getting to understand our data “They are a useful tool into identifying the important bits of data that may have previously been overlooked” is a very good point and I never thought of stats as a method of helping us see the bigger picture. Stats plus common sense in undertanding the reasoning behind the numbers helps us to make meaning out of the chaotic numbers. Next time perhaps putting a counter argument on how we dont just need stats to understand our data would make your blog even stronger.

  3. I like your analogy if a statistics jigsaw puzzle creating an image, where you can guess the result but will be able to understand all the detail of the data when it is complete. You could further develop this point. If your data supported the use of drug treatments for depression – as a random example, and you pieced all the stats pieces of the jigsaw together, you still may be missing some of the pieces in the middle! The core of the data is where it came from and the people involved. Stats may not tell you, or may not tell you clearly that loads of people dropped out, or that the drugs help but everyone felt really sick. There are details important to fully understanding the data which are better discussed in words than numbers. I think that this combined with stats which you highlighted in your blog – lets you see bits that may have been overlooked purposely by the experimenter or accidentally by a reader

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  5. Your metaphorical use of the “jigsaw” was a very good representation; however, just because the pieces fit toghether, doesnt necessarily mean that the puzzle is whole. Understanding each and every piece ultimately, should be our goal, no matter how pedantic and monotonous this may end up being – our puzzle-picture may look whole, but is it actually correct? I agree that every statistical value counts, but we shouldnt always assume that these results are correct; type 1 and type 2 errors can be devestating to our final conclusions, as well as the inputs of things such as outliers throwing off our data*. Essentially, these are the jigsaw pieces that appear to fit, but are actually from the wrong puzzle! (This is of course, all in relation to your well-used metaphor!)

    (*Shermer, Michael (2002). The Skeptic Encyclopedia of Pseudoscience 2 volume set. ABC-CLIO. p. 455. ISBN 1576076539. Retrieved January 10, 2011.)

  6. Your metaphorical use of the “jigsaw” was a very good representation; however, just because the pieces fit toghether, doesnt necessarily mean that the puzzle is whole. Understanding each and every piece ultimately, should be our goal, no matter how pedantic and monotonous this may end up being – our puzzle-picture may look whole, but is it actually correct? I agree that every statistical value counts, but we shouldnt always assume that these results are correct; type 1 and type 2 errors can be devestating to our final conclusions, as well as the inputs of things such as outliers throwing off our data*. Essentially, these are the jigsaw pieces that appear to fit, but are actually from the wrong puzzle! (This is of course, all in relation to your well-used metaphor!)

    (*Shermer, Michael (2002). The Skeptic Encyclopedia of Pseudoscience 2 volume set. ABC-CLIO. p. 455. ISBN 1576076539. Retrieved October 13, 2011.)

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  8. Hi, I love your blog – It’s one of the best I have read 🙂 It’s interesting with a lot of genuinely good points and the added humour makes it a really fun read (I especially like the pictures and I’m going to add some in my next blog post)
    Plus, you have put a good argument forward and I feel like I agree with a lot of what you have said. However, some data can’t be put neatly into categories or graphs or tables (if only stats was that easy). I look forward to your next blog 🙂

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  10. You really argue a strong point for statistics, and I like the little humourous touches, and no, I didn’t know that 100 people a year choke on ballpoint pens, but I did hear that the average person swallows 8 spiders a year, how they’re meant to work that one out I don’t know, but I found some other funnies too, for example, 40% of women have hurled footwear at men. (now that just leaves me wondering what the other 60% threw instead?) That was the first item on the list I found on this website 😉

    http://www.statisticalforecasting.com/strange-statistical-facts.php

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