Statistical Significance

Posted: August 26th, 2021

Statistical Significance

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Statistical Significance

Question 1

A statistical significance refers to the likelihood that absolute change in the relationship between or among variables is not coincidental but caused by some factors (Donoghue, 2012). Therefore, a drop in employment rate from 10.5% to 10.4% is likely not significant since there is no particular reason given that could explain the drop (Reinhart, 2015). However, from an economic perspective, the employment rate is dependent on overall changes in economic growth. Thus, a drop in employment would be attributed to a decline in economic growth (Holmes, Illowsky & Dean, 2017; Warner, 2013). Also, it could be because of an increase in the inflation rate, among other factors. Hence, the difference between 10.5% and 10.4% could only be significant if some factors were involved.

Question 2

A type 1 error occurs when a true null hypothesis is rejected. On the other hand, type II error occurs when a false null hypothesis is not rejected (Reinhart, 2015). The statistical analysis focuses on the need to minimize either of the two errors (Medawar, 2009; Reinhart, 2015). However, either error is bound to occur. Although the two errors are significantly crucial in influencing the results of a study, there are circumstances when one Type of error is serous than the other as explained in the following;

Situations when Type I error is Serious than Type II

In a given situation where an experiment establishes that it can increase the chances of getting a boy for a pregnancy. If the claim is tested and the following errors are made;

Type I Error: Couples believe that the genetic labs can influence the sex of the child while in a real sense, it cannot. Type II Error: It is stated that the genetic labs cannot influence the sex of the child when, in reality,it does. In such a situation, the more severe error is the Type I error as couples will increase the dosage of products from the genetic lab in anticipation of getting a boy, yet it is not true.

Situations when Type I error is Serious than Type II

In a given experiment, it is claimed that a drug cures at least an 85% rate of maleswho have lung cancer. For Type I: Cancer patient believes that the drug is less than 85% effective yet, in reality, it is at least 85% while for Type II: Cancer patient believes the drug treatment rate is at least 85% when the cure rate is less than 85%. Therefore, Type II error is more serious because of the consequences it holds against the user as it would significantly influence their choice, both the patient and the doctor.

References

Donoghue, P. (2012). Statistics for sport and exercise studies: an introduction. New York: Routledge.

Holmes, A., Illowsky, B. & Dean, S. (2017). Introductory business statistics. Houston, Texas: OpenStax College, Rice University.

Medawar, P. (2009). Induction and intuition in scientific thought. London New York: Routledge.

Reinhart, A. (2015). Statistics have done wrong: the woefully complete guide—San Francisco: No Starch Press.

Warner, R. (2013). Applied statistics: from bivariate through multivariate techniques. Thousand Oaks, California: SAGE Publications.