Because collecting data from every single individual in a population is often difficult or even impossible, researchers typically study a smaller group known as a sample. If the sample accurately reflects the broader population, findings from the sample can be generalized to that population.
However, selecting a truly representative sample isn’t easy—some individuals may be more likely to be included in a study than others. Sampling bias occurs when the sample differs from the population in a meaningful way. This difference can distort study results and lead to inaccurate conclusions.
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Purposive sampling—also known as judgment sampling—is a non-probability sampling technique. The researcher intentionally selects individuals to study because they possess specific traits or characteristics. In other words, the sample is chosen “on purpose” instead of randomly.
Though purposive sampling can be more convenient and less expensive than other sampling methods, it is heavily susceptible to sampling bias. Purposive sampling is common in qualitative research, particularly in fields like marketing, medical research, and other sciences.
Purposive sampling exampleA meal delivery service would like to understand the needs of potential consumers. They put together a focus group that comprises people from different demographics, such as young working professionals, parents with young children, and retirees, to see how people at different stages of life might use their product.
In research, the terms population and sample describe who you are studying. A population is the entire group you care about. A sample is a smaller group of individuals who you actually collect data from to make inferences, or educated guesses, about the population.
Population vs sample exampleElections and election polls highlight the differences between a population and a sample.
Before an election, pollsters can’t survey the whole population (every registered voter). Instead, they collect data from a sample (a small group from the population) to predict the election outcome.
If the sample is representative, it can provide a good guess of how the population will vote. If it’s biased (maybe only certain types of people respond to polls), the sample may give inaccurate results.
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Sampling methods are techniques to select a subset of individuals (the sample) from a larger group (the population). They are key to many research and statistics applications.
Sampling methods are necessary because it’s often difficult—or impossible—to study every individual in a population (imagine trying to survey every adult on Earth!). Instead, researchers focus on a smaller group, called a sample, which allows them to draw conclusions about the larger population.
To make accurate inferences about the population, it’s important to choose a sample that is representative. A representative sample closely reflects the characteristics of the population of interest.
Sampling methods can be categorized as probability or non-probability.
When writing a report or research paper, it is important to describe the sampling methods you used to select your sample. QuillBot’s free Paraphraser can help you describe your methods as precisely as possible.
Sampling exampleImagine a professor wants to understand the study habits of students at a university.
The professor could interview every single student on campus, but this would take a very long time. Instead, she selects a sample.
The professor might decide to interview the students in her seminar class. This approach would be quick and easy, but not representative (non-probability sampling).
Alternatively, she could randomly select students from the entire university (probability sampling). This approach would be more representative, but harder to coordinate.
Correlational research explores how two or more variables are statistically related. Importantly, these variables are measured as-is, without manipulation.
Correlational research can be helpful when it is unethical to manipulate variables (e.g., withholding medical treatment from someone violates research ethics) or impossible to do so (e.g., you cannot manipulate someone’s age).
Unlike experimental research, correlational research cannot establish causation. You can characterize how variables are related, but you cannot prove that changes to one cause changes to the other.
Common statistical methods to calculate correlation include Pearson’s r and regression analysis.
Correlational research exampleA researcher is interested in whether there’s a relationship between hours of sleep and academic performance.
They collect data from 100 students, recording their GPA and how many hours of sleep they get, on average, each night.
The researcher finds that students who sleep more tend to have higher GPAs (in other words, there is a strong, positive correlation between the two).
Because this is a correlational study, the researcher cannot conclude that more sleep causes higher grades. There may be other variables that are influencing these results (e.g., perhaps students with better time management sleep more and also do better in school).
PS (or P.S.) is an abbreviation for postscript, which is text written after the main body of a piece of writing.
The term postscript comes from the Latin post scriptum, which directly translates to “written after.”
PS is used at the end of a letter or an email to add further information, comments, or thoughts. This text is usually just one or two sentences or a short paragraph. It often has a friendly or playful tone.
How to use PS exampleConsider the following example of an email that uses a PS to add additional information at the end of a short email.
Hi Ava,
Just a quick note to say thanks again for your help with my presentation. It went really well, and I couldn’t have done it without your feedback!
Best,?
Aisha
PS: I forgot to mention that we’re having a team lunch next Friday. If you’re free, you should join!
Explanatory research is conducted to gain a better understanding of why something occurs. The aim of explanatory research is often to characterize a cause-and-effect relationship (i.e., how changes to an independent variable impact a dependent variable).
Explanatory research can be conducted in a naturalistic setting (by assessing the correlation between variables without attempting to change them) or in an experimental setting (by manipulating an independent variable and observing its impact on a dependent variable in a controlled environment).
Explanatory research is sometimes considered equivalent to experimental research, but more often refers to research conducted at an earlier stage in the research process, before a formal hypothesis has been created. It may more closely resemble quasi-experimental design.
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