Collecting data and study design
Sampling methods, experiment structure, and the bias terms that decide whether a conclusion generalizes.
24 terms
BlindingBlinding keeps people in an experiment from knowing which treatment a subject received, so their expectations cannot bias the response or its measurement.CensusA census is the collection of data from every single member of a population rather than from a sample.Cluster sampleA cluster sample divides the population into groups called clusters, randomly selects whole clusters, and includes every individual in the chosen clusters.Confounding variableA confounding variable is one whose effect on the response is tangled with the explanatory variable's, so you cannot separate their two influences.Control groupA control group is the set of experimental units that does not get the treatment of interest, giving a baseline to compare the treatment group against.Convenience sampleA convenience sample includes whichever individuals are easiest to reach, rather than selecting them through any random process.Double-blindA double-blind experiment hides the treatment assignment from both the subjects and the people who interact with them or measure the response.ExperimentAn experiment imposes treatments on subjects and compares their responses, supporting a cause-and-effect conclusion when treatments are assigned at random.Matched pairs designA matched pairs design compares two treatments within pairs of similar units, or one unit measured twice, then analyzes the difference inside each pair.Nonresponse biasNonresponse bias occurs when people chosen for the sample do not respond, and those who do respond differ systematically from those who do not.Observational studyAn observational study measures individuals without assigning treatments, so it can reveal associations but cannot by itself establish cause and effect.ParameterA parameter is a fixed numerical value that describes a feature of an entire population, such as its true mean or proportion.PlaceboA placebo is a dummy treatment with no active ingredient, given so a group's experience matches the real treatment except for the component being tested.PopulationA population is the entire group of individuals or objects you want to study and draw conclusions about.Random assignmentRandom assignment uses chance to sort experimental units into treatment groups, so the groups start out similar and confounding is balanced out.Response biasResponse bias occurs when respondents give systematically inaccurate answers, for example due to confusing question wording or pressure to answer a certain way.SampleA sample is the subset of a population that you actually collect data from in order to estimate something about the whole population.Simple random sampleA simple random sample is selected so that every possible sample of the given size has an equal chance of being the one chosen.StatisticA statistic is a numerical value computed from sample data, used to estimate a corresponding population parameter.Stratified random sampleA stratified random sample splits the population into similar groups called strata, then takes a separate simple random sample from each stratum.Systematic sampleA systematic sample orders the population, picks a random starting point, then selects every kth individual from that point onward.TreatmentA treatment is a specific condition applied to subjects in an experiment, whose effect on a response variable the experiment is designed to measure.UndercoverageUndercoverage is a form of sampling bias that occurs when some groups in the population are left out or underrepresented by the sampling method.Voluntary response sampleA voluntary response sample is made up of people who choose to respond on their own, such as to an open online poll or a call-in survey.