Taking this one step further, imagine your professor is conducting a study on binge drinking among college students. In other words, the results of the professor’s study don’t generalize to the overall population of the class. Would that be a representative sample of all students in the class? That would be a case of sampling error-a mismatch between the results of the sample and the true feelings of the overall class. The professor, however, only sampled individuals whose grades were over 90% in the class. Imagine a professor who takes a sample of individuals in your class to see if the material is too hard or too easy. Generalizability is a pretty easy concept to grasp. This is referred to as sampling error, the difference between results from a sample and the actual values in the population. The only way to come with a sample that perfectly reflects the population would be to include everyone in the population in your sample, which defeats the whole point of sampling! Generalizing from a sample to a population always contains some degree of error. Using random selection does not mean that the sample will be perfect.
Sampling on reason 6 generator#
Researchers often use a computer’s random number generator to determine which elements from the sampling frame get recruited into the sample.
![sampling on reason 6 sampling on reason 6](https://mtex-toolbox.github.io/images/RandomSampling_02.png)
In research, this is the principle of random selection. In order to achieve generalizability, a core principle of probability sampling is that all elements in the researcher’s sampling frame have an equal chance of being selected for inclusion in the study.
![sampling on reason 6 sampling on reason 6](https://reader012.vdocuments.net/reader012/slide/20171215/568163f8550346895dd58f24/document-11.png)
Generalizability refers to the idea that a study’s results will tell us something about a group larger than the sample from which the findings were generated. In fact, generalizability is perhaps the key feature that distinguishes probability samples from nonprobability samples. Obtaining a representative sample is important in probability sampling because of generalizability. That’s a bit of an oversimplification, but the point with representativeness is that if your population contains variations that are important to your study, your sample should contain the same sorts of variation. If, for example, you wish to be able to say something about differences between men and women at the end of your study, you better make sure that your sample doesn’t contain only women. A representative sample is one that resembles the population from which it was drawn in all the ways that are important for the research being conducted. The reason is that, in most cases, researchers who use probability sampling techniques are aiming to identify a representative sample from which to collect data. You might ask yourself why we should care about a potential participant’s likelihood of being selected for the researcher’s sample. Unlike nonprobability sampling, probability sampling refers to sampling techniques for which a person’s likelihood of being selected from the sampling frame is known. We’ll explore those unique goals and techniques in this section. The goals and techniques associated with probability samples differ from those of nonprobability samples. While there are certainly instances when quantitative researchers rely on nonprobability samples (e.g., when doing exploratory research), quantitative researchers tend to rely on probability sampling techniques. Quantitative researchers are often interested in making generalizations about groups larger than their study samples - that is, they are seeking nomothetic causal explanations.
![sampling on reason 6 sampling on reason 6](http://i.ytimg.com/vi/rsNCCQhkKN8/maxresdefault.jpg)