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Size In The Field Of Thesis

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The larger the value of "d," the larger the effect size. This means that the larger the value of "d" gets, the stronger the correlation between the two data sets is. There are many uses for effect size, in practically any field imaginable. We have already seen how they are used in advertising, although this might not be the best mathematical example. We do not have to look very far to find such an example, however. Professor Becker at the University of Colorado is actually a psychologist, and his website (2000) explains effect size using practical examples from various psychological and medical research. The efficacy of medicines and other treatments, for example, is a very important use of effect size, it can determine the suitability of risks associated with a medicine compared to its likely benefits, and give a mathematical model for when certain patients should take that medication, and when the risks outweigh the correlative benefits.

Other professions use effect size just as explicitly, though perhaps not as critically (depending on who you ask) as doctors and other medical professionals....

Economists and financial analysts almost certainly employ the use of effect size when determining the impact of certain news on a company's stock or, for instance, how long a mortgage crisis can last and how low the stock market's are likely to go based on similar events and the size of the effect felt by the markets then. Politicians, too, are sure to utilize effect size measures when planning their advertising campaigns. They don't often go the route of claiming to be "30% more effective than your last President," but determining perhaps when a message begins to lose its effect and its time to shift gears. Statistical knowledge of this can be used to forecast it, and dollars can be shifted accordingly.
Effect size is very useful, but there are limitations. Depending on the type of data being compared, it can become somewhat less reliable. It is also part of the laws of statistics, which are all built on averages and the model of a perfectly mathematical universe, which does not truly exist (or which has not been proven yet to exist). Still, it is one of the best statistical tools we…

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There are many uses for effect size, in practically any field imaginable. We have already seen how they are used in advertising, although this might not be the best mathematical example. We do not have to look very far to find such an example, however. Professor Becker at the University of Colorado is actually a psychologist, and his website (2000) explains effect size using practical examples from various psychological and medical research. The efficacy of medicines and other treatments, for example, is a very important use of effect size, it can determine the suitability of risks associated with a medicine compared to its likely benefits, and give a mathematical model for when certain patients should take that medication, and when the risks outweigh the correlative benefits.

Other professions use effect size just as explicitly, though perhaps not as critically (depending on who you ask) as doctors and other medical professionals. Economists and financial analysts almost certainly employ the use of effect size when determining the impact of certain news on a company's stock or, for instance, how long a mortgage crisis can last and how low the stock market's are likely to go based on similar events and the size of the effect felt by the markets then. Politicians, too, are sure to utilize effect size measures when planning their advertising campaigns. They don't often go the route of claiming to be "30% more effective than your last President," but determining perhaps when a message begins to lose its effect and its time to shift gears. Statistical knowledge of this can be used to forecast it, and dollars can be shifted accordingly.

Effect size is very useful, but there are limitations. Depending on the type of data being compared, it can become somewhat less reliable. It is also part of the laws of statistics, which are all built on averages and the model of a perfectly mathematical universe, which does not truly exist (or which has not been proven yet to exist). Still, it is one of the best statistical tools we have available.
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