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Sperry 8.2
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Type I & Type II Errors
There are two types of errors in that can be made in statistics, those are type I and type II. Type I is the most common error, it is when you reject the null hypothesis when it was actually correct (Erford, 2015). I will use dieting an as an example for type I, the null hypothesis states the increase in omega 3 fats will not help clients lose weight. The study then shows that the null hypothesis should had been retained.
Type II rejects the alternative hypothesis when it is actually true (Erford, 2015). For this example I will use a dieting drug called Zappo. The null hypothesis stated that participants using Zappo will not see more than a 5% body fat lose. Research is conducted and the results show that participants saw an average of 2.5% body fat loss.
In my review of both type I and type II errors I find that type II has more of a negative effect on clients/participants. For instance, in my type II example I gave if the error was not found then Zappo when have been advertised has seeing a 5% loss of body fat, but consumers never saw the loss that was promised. Now compare that to the omega 3 example, which would have been a pleasant discovery.
Pomajzl 8.2
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Type I error is when there are significant differences between groups of a study but, no differences within the entire actual population (Erford, 2015). Type II error is the opposite. It is when significant differences did not exist between groups but actually did exist within the entire population (Erford, 2015).
I feel that both type I error and type II error can be costly to human services and counseling. With an error, either procedures or treatment may get recognized ineffectively or they may not get the credit that is needed to be used with clients. Both of these can be costly when it comes to clients. As I have stated in past posts, each person is different and without being focused on one specific, it is difficult to treat all clients when diff
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