2
$\begingroup$

Today, I read a post Computing and Sustainability: What Can Be Done?. And I found that the author of this post can easily find statistical problems in other fields, such as computer science. Since as a stats graduate, it is very important to recognize statistical questions involved in other fields, I am really curious how to figure out the statistical questions effectively. What are the characteristics for any problems to be recognized as statistical?

$\endgroup$
  • 4
    $\begingroup$ ----- Data ----- $\endgroup$ – user10525 Jul 2 '12 at 15:39
  • $\begingroup$ If you got the sequence data of one sample, what kind of statistical problems you could raise? $\endgroup$ – Honglang Wang Jul 2 '12 at 16:08
  • $\begingroup$ @Honglang Pretty much any question you can ask of data, you can legitimately ask of one value--the real issue is what answers can you come up with :-). To see that this is not a trivial point, please read the example of a one-value confidence interval. $\endgroup$ – whuber Jul 2 '12 at 17:00
  • 1
    $\begingroup$ @whuber Indeed I think that the distinction between ML, DM and Stats to be different fields rather than sides of the same dice is mostly superficial and an unfortunate result of historic processes. But then the fields grow together more and more anyway (hasn't this been discussed elsewhere here?) However, your definition would make the fields being the same anyway but also showering ;-) Although, now that I think of it some mathematical statistics might be done data-free? $\endgroup$ – Momo Jul 2 '12 at 20:09
  • 1
    $\begingroup$ To me data is not enough to make a problem statistical. Suppose the data fit a sine wave perfectly. That may be interesting mathematically but it has no relevance in statistics. What makes a problem statistical is random variability. You may be looking to discover a functional relationship between variables but the relationship is not immediately apparent becaues of the random noise. Through smooth, filtering or averaging statistics allows you to see the signal through the noise. Problems with noisy data are abundant in all fields and that is why statistics is so useful. $\endgroup$ – Michael R. Chernick Jul 2 '12 at 22:41

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.