One of the major motivations of statistics is to attempt to figure out whether there is a link or a lack thereof between something and something else.
The "somethings" tend to be described in data format, and therefore mathematical procedures come in very handy to make an inference and to support or reject what common sense is suggesting.
Whereas for numeric data the methods are quite straightforward, as they stand on the shouders of giants as taking into account the knowledge coming from, say, physics, geometry, calculus, differential equations etc., when one has to deal with categorial data, the things get somewhat trickier.
There is an ample bunch of test fo dependence/independence for categorical data, and there are well-written books on the matter - like this one or this one. Nonetheless, the suggestion to use this or that particular test is often driven by empirical knowledge and looks more like a technical analysis omen rather then a thorough, sigma-algebra-based strict mathematical stuff.
Whatever works.
As searching for yet another test to evaluate the existence of a link between an important health ouctome and a common exposure factor, I´ve bumped into a classic study conducted by the great mind behind it all, Karl Pearson. In 1909, he evaluated whether there is a connection between criminal behavior and consumption of alcoholic beverages.
Pearson studied 1426 criminals, and his null hypothesis was that there was no association between the type of crime and alcohol consumption.
Below there is a descriptive table for this study. It has been taken from the book by A. Elliott and W. Voodward, Statistical Analysis Quick Reference Guidebook: With SPSS Examples
Just by a simple 'look see' method, one can firmly reject the null hypothesis: drinkers are clearly more prone to conducting criminal activities.
Except for one. Fraud. Indeed, fraud should require some solid intellectual input, and therefore one must be really clear-headed when doing something fradulent.
As actions of this kind are most frequently associated with financial industry, HR departments of relevant institutions could take a closer look at this interesting misalignment in the common pattern. Also, the prospective candidates for financial jobs could abstain from proudly proclaiming selves as devoted no-drinkers during the interviews.
Not only this is rude, because, you know, in this industry people do not drink alcohol, but also the admirers of Pearson's contribution to statistics might find these life choices not so zero cool.
I'm kidding, of course.
The value of the chi-square statistics for the test of independence is 49.731 and the p-value is around 0.000.
The "somethings" tend to be described in data format, and therefore mathematical procedures come in very handy to make an inference and to support or reject what common sense is suggesting.
Whereas for numeric data the methods are quite straightforward, as they stand on the shouders of giants as taking into account the knowledge coming from, say, physics, geometry, calculus, differential equations etc., when one has to deal with categorial data, the things get somewhat trickier.
There is an ample bunch of test fo dependence/independence for categorical data, and there are well-written books on the matter - like this one or this one. Nonetheless, the suggestion to use this or that particular test is often driven by empirical knowledge and looks more like a technical analysis omen rather then a thorough, sigma-algebra-based strict mathematical stuff.
Whatever works.
As searching for yet another test to evaluate the existence of a link between an important health ouctome and a common exposure factor, I´ve bumped into a classic study conducted by the great mind behind it all, Karl Pearson. In 1909, he evaluated whether there is a connection between criminal behavior and consumption of alcoholic beverages.
Pearson studied 1426 criminals, and his null hypothesis was that there was no association between the type of crime and alcohol consumption.
Below there is a descriptive table for this study. It has been taken from the book by A. Elliott and W. Voodward, Statistical Analysis Quick Reference Guidebook: With SPSS Examples
Just by a simple 'look see' method, one can firmly reject the null hypothesis: drinkers are clearly more prone to conducting criminal activities.
Except for one. Fraud. Indeed, fraud should require some solid intellectual input, and therefore one must be really clear-headed when doing something fradulent.
As actions of this kind are most frequently associated with financial industry, HR departments of relevant institutions could take a closer look at this interesting misalignment in the common pattern. Also, the prospective candidates for financial jobs could abstain from proudly proclaiming selves as devoted no-drinkers during the interviews.
Not only this is rude, because, you know, in this industry people do not drink alcohol, but also the admirers of Pearson's contribution to statistics might find these life choices not so zero cool.
I'm kidding, of course.
The value of the chi-square statistics for the test of independence is 49.731 and the p-value is around 0.000.
No hay comentarios:
Publicar un comentario