Basic Applied Statistics Quiz #1 2023 with verified questions and answers

categorical variable qualitative - does not follow the rules of math quantitative variable can calculate mean value five possible situations of variable types 1. two categorical 2. two quantitative 3. one categorical 4. one quantitative 5. one categorical and one quantitative summarizing categorical data count, proportion, percentage count number of individuals in a category proportion count in category divided by total number of individuals considered percentage proportion as decimal x 100 data recorded values of categorical or quantitative variables statistics science concerned with gathering data, displaying and summarizing data, and using information from data to draw conclusions about a larger group relationships between two variables explanatory or response random occurrence an occurrence that happens by chance alone, and not according to a preference or an attempted influence probability a formal study of the chance of occurring in a random situation statistical inference drawing conclusions about population based on sample data production take sample data from the population, with sampling and study designs that avoid bias displaying and summarizing use appropriate displays and summaries of the sample data, according to variable types and roles probability assume we know what's true for the population: how should random samples behave? statistical inference assume we only know what's true about sample values of a single variable or relationship bias tendency of an estimate to deviate on from direction from a true value selection bias due to unrepresentative sample, rather than a flawed study design sampling frame doesn't match population self-selected volunteer -- favors strong opinions haphazard sample does not make sense convenience sample easy to pick, lazy non-response no response to questions probability sampling plan incorporates randomness in the selection process so rules of probability apply simple random sample taken at random without replacement systematic sampling plan uses methodical but non-random approach -- select individuals at regularly spaced intervals on a list issues to consider for any study design 1. sample size 2. errors in study's conclusions prospective study researchers record variables' values forward in time from the present retrospective study researchers record variables' values backward in time about the past confounding variable one that confuses the issue of causation because its values are tied in with those of the explanatory variable, and also play a role in response variable's values two-sample design compares responses for two independent groups paired design a pair of response values recorded for each unit 1. before and after 2. compare responses between twins, siblings, or married couples factor an explanatory variable in an experiment treatment value of explanatory variable imposed by researchers subjects individuals are human may have no control group if all subjects must be treated if simulated treatment is risky if the experiment is poorly designed three meanings of control 1. we control for a confounding variable in an observational study by separating it out 2. researchers control who gets what treatment in an experiment by making the assignment themselves, ideally at random 3. the control group in an experiment consists of individuals who do not receive a treatment per se, or who are assigned a baseline value of the explanatory variable