Sometimes however, the researcher is just interested in predicting the probability of an event or a disease given some risk factors. Impracticality when answering non-causal questions:Ī randomized trial is our best bet when the question is to find the causal effect of a treatment or a risk factor. Higher cost of implementation:Īn experimental design with random assignment is typically more expensive than observational studies where the investigator’s role is just to observe events without intervening.Įxperimental designs also typically take a lot of time to implement, and therefore are less practical when a quick answer is needed. In the real world, people who take the treatment might be very different from those who don’t – so the assignment of participants is not a random event, but rather under the influence of all sort of external factors.Įxternal validity can be also jeopardized in cases where not all participants are eligible or willing to accept the terms of the study. the generalizability of the study results) is compromised because the results of a study that uses random assignment represent what would happen under “ideal” experimental conditions, which is in general very different from what happens at the population level. With random assignment, external validity (i.e. Randomization is ethical only if the researcher has no evidence that one treatment is superior to the other.Īlso, it would be unethical to randomly assign participants to harmful exposures such as smoking or dangerous chemicals. But the real benefit of random assignment will be when data is aggregated in a meta-analysis. This means that the results of a randomized trial will sometimes be wrong, and this is absolutely okay.Īlthough the results of a particular randomized study are unbiased, they will still be affected by a sampling error due to chance. So for each individual study, differences between the treatment and control group will exist and will influence the study results. But do not forget that scientific evidence is a long and continuous process, and the groups will tend to be equal in the long run when a meta-analysis aggregates the results of a large number of randomized studies. So randomization will not produce perfectly equal groups for each specific study, especially if the study has a small sample size. But it will approach 1/6 if you repeat the experiment a very large number of times and calculate the average number of times the specific outcome turned up. This is similar to throwing a die: If you throw it 10 times, the chance of getting a specific outcome will not be 1/6. If you want 2 perfectly equal groups, you better match them manually as is done in a matched pairs design (for more information see my article on matched pairs design). This is because when dealing with randomization there is still an element of luck. it produces comparable groups, but it does not guarantee the equality of these groups.Ī more complete answer: Randomization will not and cannot create 2 equal groups regarding each and every characteristic. Short answer: This is perfectly normal, since randomization only assures an unbiased assignment of participants to groups, i.e. Question: What should you do if after randomly assigning participants, it turned out that the 2 groups still differ in participants’ characteristics? More precisely, what if randomization accidentally did not balance risk factors that can be alternative explanations between the 2 groups? (For example, if one group includes more male participants, or sicker, or older people than the other group). What if random assignment produced unequal groups? Note that randomization does not prevent these effects from happening, it just allows us to control them by reducing their risk of being associated with the treatment. This effect can bias the study since it represents an alternative explanation of the outcome. ![]() Regression to the mean: This happens when the participants’ outcome score is exceptionally good on a pre-treatment measurement, so the post-treatment measurement scores will naturally regress toward the mean - in simple terms, regression happens since an exceptional performance is hard to maintain.participants becoming wiser, hungrier, or more stressed with time) which might influence the outcome. ![]()
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