Practical Significance vs. Statistical Significance - Essay Example

A type-II is a direct opposite where the researcher may totally miss out on a possible impacting result or hypothesis and eventually says that there were no critical results. The example of type II error is just the converse of the previous example where the result turns out to be negative for a disease but actually, the patient does possess it. Many of the time, these types of errors occur because of incorrect methods followed in research, like the incorrect number of sample data being tested, etc.

Because of the impact produced by these errors which sometimes may result catastrophically, there should be immense attention placed on such predictions to prevent both false-positive and false-negative results. (Type I and Type II errors, 2004) Statistical significance and practical significance are although based out of data, they have completely different interpretations. Statistical significance provides a relationship between data which may be significant statistically but does not count in a useful way, practically.

However, practical significance, as the name indicates, provides a meaningful relationship between the data which may go on to help in identifying or stating a hypothesis. For example, if we consider the number of educated people in a state over a period of time and if it results in such a way that on mean, 60 people are educated out of 100 in one state and 61 people are educated out of 100 in another state. Statistically it provides a lot of difference between two sets of data but on the other hand, there is no significance associated with it practically.

(McIntyre, n.d) References Type I And Type II errors. (2004). The Virtual Psychology Classroom. Retrieved from: http://allpsych.com/researchmethods/errors.html McIntyre, W. (n.d). Practical Significance Versus Statistical Significance. Retrieved from: http://EzineArticles.com/3657116