In the context of epidemiology and biostatistics, a population is the entire group that you base your research question on, such as all the people in a certain city or all Americans over the age of 65. To study a population, researchers often use a smaller subset, known as a sample, which should represent the larger population. There are various methods to sample a group, such as convenience sampling (based on ease and proximity) and random sampling (where everyone has an equal chance of being selected), with random sampling being the preferred method for obtaining a representative sample.
It is important to choose a sufficiently large sample to increase the likelihood of obtaining a representative sample. Researchers need to consider factors such as whether the sample properly represents the population, whether any traits are over- or under-represented, and any issues with sample representativeness due to sample size or sampling methods. The goal is to make inferences about the defined population from the studied sample using various statistical methods. By understanding the concepts of populations, samples, and inference, researchers can better analyze data and draw accurate conclusions about the population being studied.
<ul> <li>Understanding Populations</li> <ul> <li>A population is the entire group based on your research question</li> </ul> <li>Introduction to Sampling</li> <ul> <li>Sampling is often necessary, due to the large size of defined populations</li> <li>A sample as a smaller subset of a defined population</li> <li>A sample should accurately represent the larger population on relevant traits/characteristics</li> </ul> <li>Methods of Sampling</li> <ul> <li>Convenience Sampling <ul> <li>Based on ease and proximity</li> <li>Ease and proximity can introduce bias (for example, unrepresentative neighborhoods in a city)</li> </ul> <li>Random Sampling <ul> <li>All in the population has equal chance of being selected</li> <li>Can be better at maintaining a balanced representation of the population</li> </ul> </ul> <li>Importance of Sample Size</li> <ul> <li>Explanation of why the size of a sample matters: decreases error</li> <li>Comparison of sampling to a coin toss: over many coin tosses, the probability of "heads" will be closer to 50% (may not be the case with a smaller sample)</li> <li>Larger sample also increases the chance of selecting a wide range of traits and characteristics</li> </ul> <li>Inferential Statistics</li> <ul> <li>Overall goal is to make inferences about the population based on studying the sample</li> <li>Examples of inferential statistics: confidence intervals and hypothesis tests</li> </ul> </ul>
In epidemiological and biostatistical research, the population represents the total group of interest, such as individuals with a specific disease. Researchers often can't study the entire population due to time, cost, ethical, and feasibility constraints. Instead, they select a sample, which is a subset of individuals from the population, to study. Researchers use statistical techniques to make inferences from the sample back to the population as a whole.
Convenience sampling is a non-probabilistic method of selecting subjects that are easily accessible and available. Although it is an easy and less expensive method, it often introduces bias because the selection of the subjects does not accurately represent the entire population. In contrast, random sampling is a probabilistic method in which each individual in the population has an equal chance of being selected, thus promoting a more representative sample and reducing selection bias. Random sampling is preferred, especially in large-scale studies, but may not always be feasible due to resource limitations or other constraints.
The sample size, or the number of research subjects within a sample, plays an important role in the representativeness of the sample. In general, a larger sample size tends to increase the likelihood that a sample mirrors the characteristics of the population as a whole. Larger sample sizes also provide more stable and accurate inferential statistics. However, collecting data from a large sample may be more time-consuming, costly, and challenging. Therefore, researchers must carefully weigh these factors and consider the purpose of the study to determine an appropriate sample size.
Sample representativeness refers to how well the sample reflects the characteristics of the target population. Factors that can influence representativeness include the sampling method, sample size, accessibility of the population, and potential sources of bias. To achieve a representative sample, researchers must select suitable strategies for recruiting subjects, consider the appropriate sample size, and account for potential sources of bias. This, in turn, can enhance the external validity or generalizability of the research findings.
Inferential statistics are used to draw conclusions or make inferences about a population based on a sample. Through various statistical methods, researchers are able to estimate population parameters and test hypotheses by analyzing sample data. Inferential statistics help determine the likelihood that the findings from a sample can be generalized to the broader population. Confidence intervals and hypothesis tests, such as t-tests or chi-square tests, are common examples of inferential statistics used in epidemiological and biostatistical research.