Though science has a clear methodology that researchers have virtually perfected over centuries, rarely is an individual study perfect. Studies usually have at least one limitation that makes some aspects of their results less likely to be accurate, such as the hypothesis not being proved though it might be true, the introduction of bias, a necessity to rely on estimates for some data, or limitations on the scope and applicability of the study. Whatever the case, a good scientist knows the potential sources of limitations and mentions those influential ones in her paper.

Science Doesn’t Tool Around

A scientist’s toolbox is as strong as its weakest link. Sometimes it’s the tools scientists use that introduce limitations to the study. Contemporary science makes heavy use of tools, which has led to studies that weren’t possible in the past, like the observation of quantum particles in physics. But how well a study reports its results relates to how strong the instruments are. For example, microscopes have limitations on what they measure. While you can use a microscope to view a two-dimensional image of bacteria and record results from that image, you won’t be able to record the height of the bacteria. If one thing you are reporting in your study is the volume of bacteria, you will have to estimate the height and multiply it by the observed area to get a volume. In this way, your microscope limits the accuracy of your study’s results.

Subjects’ Defects

From physics to medicine, scientists employ “subjects,” or the objects of observation, in their studies. But for most sciences, the traits of subjects are rarely consistent. The lack of consistence in the subjects can lead to problems drawing conclusions. For example, in medicinal studies, doctors might give one of two chosen drugs to two groups of people. The doctors probably tried to make sure that these two groups are similar in demographics, such as having a similar mix of ages, genders and health statuses. But small differences, such as lifestyle and genetic factors can skew the effects of the drugs on the subjects. In addition, because scientists across the globe have different sources and amounts of funding, not all scientists can use large groups of subjects. A zoologist, for example, might not be able to acquire many chimpanzees for her study. But because small sample sizes make the statistics of a study less dependable, the results of a study that lacks sufficient funding might not be strong or mathematically significant.

Acts of Nature

Scientists are still human. Once in a while, a scientist might make a mistake, become ill and pause the study, or run into technical issues. A scientist who suffers such problems will likely report it in his paper, citing them as limitations. Regent University mentions that limitations outside of a scientist’s control are remarkably common. For example, a botanist researching the relationship between the amount of pests on a specific plant and the amount of sunlight that plant receives might have missing data if he finds several plants to be missing next time he reaches his plot of land. He might suspect the plants were eaten by a wild herbivore. In this case, his sample size has been reduced by phenomena he cannot control, which can weaken his study.

Confound It!

One thing that can really limit a study and frustrate a scientist, especially in well-designed experiments, is the confounder. A confounder is a quality or variable that affects the results of the study but is not included in the study itself. A scientist often notices a confounder only during or after the experiment. She lists that confounder as a limitation in her report. Confounders include environmental factors, neglected differences between subjects, and unexpected changes during the experiment. For example, a scientist working with lab rats might not concern herself whether the lab rats in her study are kept in room A or room B of the laboratory. So, she keeps some in each room. But at the end of the experiment, she might notice that the lab rats in room A are significantly fatter than those in room B. In this experiment, the room the lab rats are kept in is a confounding factor. She might not have noticed that the temperature in room B is lower or that the lights in room A are dimmer. Regardless, she will likely cite the environment as a confounder, letting other scientists know that future studies should keep the lab rats in the same room.