However, due to the lack of a pre-test procedure, it is impossible to assess whether the treatment had any effect in the sample at all. In the one group pretest posttest study the variable under study is measured among the sample prior to the treatment, to get the baseline figures, and then measured again among the same sample after the treatment to be compared with the baseline figures. Nevertheless, the results may not be conclusive that the change was effected by the treatment or some other unknown variable. Meanwhile, the static group comparison study compares the post-test results of two samples (one with treatment and one without treatment). However, just like in the one shot case study, the lack of a pre-test for both samples makes it impossible to conclude whether a change has occurred or not within the groups. In everyday life, people make conclusions using pre-experimental research all the time without even realizing it. For example, claiming that drinking milk enhances intelligence of children because a certain gifted child is a milk drinker is an example of a one shot case study. Meanwhile, concluding that the use of a slimming pill caused a group of people to lose weight can be considered as a one group pretest posttest study. In the same way, assuming that a certain hair conditioner is the reason why the hair of those who use it is shinier than those who don't is a form of static group comparison study.

III. Quasi-Experimental Design

Quasi-experimental design is closer to true experimental design compared to pre-experimental design because it has a means of comparing groups (i.e. It utilizes the control group concept). However, it uses convenience and judgment sampling instead of random sampling and therefore runs the risk of having a biased sample. There are three types of quasi-experimental design according to Barry Gribbons and Joan Herman namely nonequivalent groups -- posttest only, nonequivalent groups -- pretest-posttest and time series designs.

The major limitation of this design is it does not take into account the difference between the two groups prior to the treatment. On the other hand, nonequivalent groups -- pretest-posttest eliminates this major limitation by testing the differences between the two groups prior to the treatment. However, this still does not guarantee that the difference in results may be attributed to the different treatments applied between the two groups. Lastly, time series designs calls for two or more pretest and posttest to provide a better picture of the results through trends.
Quasi-experimental designs are used when a true random sample is either impractical to do or impossible to attain. This problem usually arises if the target population has rare qualities. For example, if the research involves people born as triplets, the researcher will probably include all triplets encountered to attain a fairly sized sample. In addition, budget and timing should also be considered in deciding which approach to take since quasi-experimental design is obviously cheaper and can be accomplished faster than a true experimental design. Nevertheless, the accuracy and precision that a true experimental design offers is paramount.