In the various fields of medical research, a study population is a sample of patients taken from a larger group of people who normally share a similar characteristic, including sex, age, or illness. This sample can be studied for several different reasons, including their response to an experiment or the potential risk of acquiring a certain disease. But in many cases, the study population is used to study the effects of a new treatment, device, or medication on a specific group of patients. Knowing what is in the water when you put the latest pill in your mouth is important.
Studies that require large numbers of participants to detect a statistically significant difference are often performed with probability sampling. Probability sampling involves a process in which the researchers collect a random sample of individuals, and then use mathematical models to simulate the characteristics of the sample so that statistical comparison of those characteristics can be made. The study population chosen will have characteristics that closely resemble the characteristics of the general sample. If the researchers find a significantly significant difference between the characteristics of the sample and the characteristics of the entire population, they can conclude that treatment is more effective than a placebo. If they find no difference, however, they cannot conclude that the new drug is ineffective.
A drawback to using probability sampling is that it relies on the observer’s ability to remember and report the characteristics of the sample accurately. It, therefore, relies on the honesty of the individuals participating. Even when taking place under controlled conditions, however, it is nearly impossible to ensure the accuracy of any individual’s responses. In addition, even when providing data on how likely a treatment is to be effective, or why one treatment is preferred over another, researchers can only estimate the effectiveness of any treatments by assessing the effects on a subset of patients. Thus, estimates of effectiveness are based on educated guesses rather than on hard empirical data.
Using a random sample of patients with known medical conditions to assess treatments is an alternative to what is a study population. For instance, healthcare organizations frequently use such sampling methods to assess the effectiveness of asthma treatments in random samples of patients with asthma. A doctor or administrator asks a series of questions about demographics, medical history, and symptoms during an in-office visit. If the answers from the patient match the data supplied by the patient, then the doctor knows the likely effectiveness of the treatment. A sample of relatively normal people with relatively normal symptoms can provide the same information as a sample of asthma patients who have had their symptoms diagnosed and are undergoing treatment.
In addition to using a sample of normal people, healthcare organizations also sometimes use samples that are drawn from a smaller subset of the population. This is called a targeted sample. A sample of a smaller group provides greater statistical power because there is a higher frequency of similarities between the sample and the target population. A smaller population also allows researchers to make more precise estimates of the statistical parameters. Because the smaller sample size is drawn from a smaller group, there is less room for error when determining sample size; consequently, it makes more sense to choose a smaller sample size when determining what is a study population.
Benefits of Random Sampling
Another benefit of using a random sampling technique is that it saves time. Sampling from a larger population enables researchers to make more accurate inferences about the characteristics of that population, which are not readily available for a smaller, more limited group. Random sampling saves time because researchers don’t have to consider whether every person who responds to an invitation to participate in a study is qualified for the study. Instead, they choose individuals at random, without considering whether any person might not be appropriate for the sample.
A final benefit of a sample selection procedure for determining what is a study population relates to the confidentiality of the results. Since a larger portion of the population is included in the sample, fewer people are allowed to see the results of the study and, therefore, fewer could potentially alter the statistical methods used to measure an individual’s characteristics. A smaller, limited population is not allowed to see this data, as fewer individuals would be able to interpret the data.
If a small group is chosen to represent the sample, then there will be more likelihood of finding discrepancies between that population and the true population for a given question or result. Large sample sizes help resolve this problem, because a smaller number of questions can be answered with a high level of accuracy than if there are too many people in a sample. For this reason, choosing a proper sample size is important in research.