Making statistics intuitive
A sampling frame lists all members of the population you’re studying. Your target population is the general concept of the group you’re assessing, while a sampling frame specifically lists all population members and how to contact them. It might also include demographic information for each person because some methods, such as stratified sampling, require it. [Read more…] about Sampling Frame: Definition & Examples
Selection bias occurs when researchers make decisions that cause a sample to be systematically different from the population of interest.
Selection bias can arise from various decisions, such as:
Population vs sample is a crucial distinction in statistics. Typically, researchers use samples to learn about populations. Let’s explore the differences between these concepts! [Read more…] about Population vs Sample: Uses and Examples
Political polls provide crucial information during elections. When done correctly, small samples can predict the outcomes. We’re approaching election time here in the U.S., and campaigns are switching into high gear along with political polls!
You’d think that conducting a poll is straightforward. You ask a bunch of people who they’ll vote for and count the results, right? But it’s not quite that simple.
For starters, political polls face the same challenges as other polls. However, a poll taken for political reasons also has some unique issues. When pollsters do everything correctly, the small, sampled group accurately reflects the entire population. The trick is to do it correctly!
In this post, learn how statistical concepts apply to political polls and the extra difficulties they face. [Read more…] about Political Polls and Their Challenges
Survivorship bias, or survivor bias, occurs when you tend to assess successful outcomes and disregard failures. This sampling bias paints a rosier picture of reality than is warranted by skewing the mean results upward. [Read more…] about Survivorship Bias: Definition, Examples & Avoiding
Sampling bias in statistics occurs when a sample does not accurately represent the characteristics of the population from which it was drawn. When this bias occurs, sample attributes are systematically different from the actual population values. Hence, sampling bias produces a distorted view of the population. Sampling bias often involves human subjects, but it can also apply to samples of objects and animals. Medical researchers refer to this problem as ascertainment bias. [Read more…] about Sampling Bias: Definition & Examples
Purposive sampling is a non-probability method for obtaining a sample where researchers use their expertise to choose specific participants that will help the study meet its goals. These subjects have particular characteristics that the researchers need to evaluate their research question. In other words, the researchers pick the participants “on purpose.” [Read more…] about Purposive Sampling: Definition & Examples
Snowball sampling is a non-probability method for acquiring a sample that uses participants to recruit additional participants. Researchers call it snowball sampling because if the initial participant recruits two more, and those two recruits each bring in two more, and so on, the number of participants can grow exponentially like a rolling snowball. This method is also known as chain sampling or network sampling. [Read more…] about Snowball Sampling: Definition and Example
Quota sampling is a non-random selection of subjects from population subgroups that the researchers define. Researchers use quota sampling when random sampling isn’t feasible, and they want more control over who they select compared to other non-probability methods, such as convenience sampling. [Read more…] about Quota Sampling: Definition & Examples
A representative sample is one where the individuals in the sample reflect the properties of an entire population. Use a representative sample when you want to generalize the results from the sample to a population. By studying a representative sample, you can approximate the properties of the population from which it was drawn. [Read more…] about Representative Sample: Definition, Uses & Methods
Sampling methods are the processes by which you draw a sample from a population. When performing research, you’re typically interested in the results for an entire population. Unfortunately, they are almost always too large to study fully. Consequently, researchers use samples to draw conclusions about a population—the process of making statistical inferences. [Read more…] about Sampling Methods: Different Types in Research
Simple random sampling (SRS) is a probability sampling method where researchers randomly choose participants from a population. All population members have an equal probability of being selected. This method tends to produce representative, unbiased samples. [Read more…] about Simple Random Sampling: Definition & Examples
Convenience sampling is a non-probability sampling method where researchers use subjects who are easy to contact and obtain their participation. Researchers find participants in the most accessible places, and they impose no inclusion requirements. Convenience sampling is also known as opportunity or availability sampling. [Read more…] about Convenience Sampling: Definition & Examples
Systematic sampling is a probability sampling method for obtaining a representative sample from a population. To use this method, researchers start at a random point and then select subjects at regular intervals of every nth member of the population. Like other probability sampling methods, the researchers must identify their population of interest before sampling from it. [Read more…] about Systematic Sampling: Definition, Advantages & Examples
Cluster sampling is a method of obtaining a representative sample from a population that researchers have divided into groups. An individual cluster is a subgroup that mirrors the diversity of the whole population while the set of clusters are similar to each other. Typically, researchers use this approach when studying large, geographically dispersed populations because it is a cost-controlling measure. [Read more…] about Cluster Sampling: Definition, Advantages & Examples
Stratified sampling is a method of obtaining a representative sample from a population that researchers have divided into relatively similar subpopulations (strata). Researchers use stratified sampling to ensure specific subgroups are present in their sample. It also helps them obtain precise estimates of each group’s characteristics. Many surveys use this method to understand differences between subpopulations better. Stratified sampling is also known as stratified random sampling. [Read more…] about Stratified Sampling: Definition, Advantages & Examples