An outlying observation, or outlier, is one that appears to deviate markedly from other members of the sample in which it occurs (Grubbs, 1969). Outliers may appear in Psychology researches, and affecting the analysis of the data into conclusions. This decreases the validity of the results, making it hard to generalise and apply to the target areas.
To encounter the problem, researchers would normally eliminate the extreme scores. This will make the total set of data to have a clearer sense of direction on the effects of relationships. However, excluding outliers is a reductionist approach, and may prevent us to develop our understanding by removing a special case of data. Furthermore, it is possible that the researchers may not include outliers as to increase the validity of the research. This is researchers biased.
In some cases, outliers can appear because of errors. The errors can be created from many parts of the research. Outliers can be created by few individual participants not understanding the procedures, and provide extreme scores when responding to the conditions. This can be solved by conducting a pilot study before the research. A pilot study is a rehearsal of the research in a small scale, and it helps to identify any possible problems the research may face when being carried out in a larger scale.
Outliers can also be created when there are errors in analysing and concluding the data. An example can be seen with Quantitative Data. It is possible that the set of data has been put in incorrectly during calculation, and affect the results of Mean, Median and Standard Deviation. A good method to deal with the problem is to reject all values beyond a criterion number of standard deviations from the mean of each experimental cell; typically discarding all values beyond about 2 to 3 SD of the mean (Selst & Jolicoeur,1994). Researchers may also use Inter-quartile range of the data set to remove extreme scores, by only focusing on the mid 25-75%. It is useful because according to the Normal Distribution, majority of the population lie in the middle range, with the minority of population lie within the extreme ends.
Outliers can be problems to researches, by being the unconventional data which differ from the majority. It is difficult to decide if it is appropriate to remove them. The advantage of removing outliers is that it makes it easier to evaluate the data, without extreme scores. The disadvantage of removing them is that it may make us ignore some special but important data, stopping us to provoke with developing an improved idea on the targeted topic.
Selst, M.V. & Jolicoeur,P. (1994): A solution to the effect of sample size on outlier elimination, The Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, 47:3,632.
Grubbs, F. E. (1969): Procedures for detecting outlying observations in samples. Technometrics 11, 1–21.