Making statistics intuitive
The placebo effect occurs when a fake medical treatment produces real medical benefits psychosomatically. In short, believing in the treatment and the power of the mind can help someone feel better. The placebo effect can be so powerful that it mimics genuine medicine. Consequently, scientists need to control for it when conducting clinical trials. [Read more…] about Placebo Effect Overview: Definition & Examples
A randomized controlled trial (RCT) is a prospective experimental design that randomly assigns participants to an experimental or control group. RCTs are the gold standard for establishing causal relationships and ruling out confounding variables and selection bias. Researchers must be able to control who receives the treatments and who are the controls to use this design. [Read more…] about Randomized Controlled Trial (RCT) Overview
A prospective study is an experimental design that looks forward in time and observes events as they happen. Participants begin the study without having a condition of interest. Then researchers gather data and take measurements at regular intervals to identify the occurrence of specific outcomes along with other data that might relate to them. [Read more…] about Prospective Study: Definition, Benefits & Examples
Ecological validity refers to how accurately researchers can generalize a study’s findings to real-world situations. Simply put, it measures how closely an experiment reflects the behaviors and experiences of individuals in their natural environment. [Read more…] about Ecological Validity: Definition & Why It Matters
A lurking variable is a variable that researchers do not include in a statistical analysis, but it can still affect the outcome. These variables can create problems by biasing your statistical results in any of the following ways:
Learn more about Spurious Correlations. [Read more…] about Lurking Variable: Definition & Examples
Probability sampling is the process of selecting a sample using random sampling. When you use this method, each individual or unit in a population has a known, non-zero chance of being randomly selected for the sample. Statisticians consider this method the most reliable because it tends to minimize sampling bias and produce samples that accurately represent the entire population. A representative sample allows you to use the sample to make inferences about the population. [Read more…] about Probability Sampling Overview
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:
Undercoverage bias occurs when the population list from which the researchers select their sample (aka the sampling frame) does not include all population members. When that happens, the sample cannot contain the unlisted individuals, potentially producing a biased sample that doesn’t fully represent the population. [Read more…] about Undercoverage Bias: Definition & Examples
A matched pairs design is an experimental design where researchers match pairs of participants by relevant characteristics. Then the researchers randomly assign one person from each pair to the treatment group and the other to the control group. This type of experiment is also known as a matching pairs design. [Read more…] about Matched Pairs Design: Uses & Examples
Nonresponse bias occurs when people who do not participate in a survey or study have different characteristics or opinions than those who do participate. In this situation, the sample data overrepresent the subpopulations who tend to respond instead of reflecting the whole population. [Read more…] about Nonresponse Bias: Definition & Reducing
Concurrent validity is the degree to which assessment scores correlate with a criterion variable when researchers measure both variables at approximately the same time (i.e., concurrently). This method validates an assessment instrument by comparing its scores to another test or variable that researchers had validated previously. [Read more…] about Concurrent Validity: Definition, Assessing & Examples
Criterion validity (aka criterion related validity) is the degree to which scores from a construct assessment correlate with a manifestation of that construct in the real world (the criterion). [Read more…] about Criterion Validity: Definition, Assessing & Examples
Predictive validity is the degree to which a test score or construct scale predicts a criterion variable measuring a future outcome, behavior, or performance. Evaluating predictive validity involves assessing the correlation between the pre-test score and the subsequent criterion outcome. [Read more…] about Predictive Validity: Definition, Assessing & Examples
A retrospective study an experimental design that looks back in time and assesses events that have already occurred. The researchers already know the outcome for each subject when the project starts. Instead of recording data going forward as events happen, these studies use participant recollection and data that were previously recorded for reasons not relating to the project. These studies typically don’t follow patients into the future. [Read more…] about Retrospective Study: Definition & 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