AP Stats Unit 1 Quiz

census: collects data from every individual in the population.;

Sample survey: selects a sample from the population of all individuals about which we desire information. The goal of a sample survey is inference: we draw conclusions about the population based on data from the sample. It is important to specify exactly what population you are interested in and what variables you will measure.;

Convenience samples: choose individuals who are easiest to reach. In voluntary response samples, individuals choose to join the sample in response to an open invitation. Both of these sampling methods usually lead to bias: they consistently underestimate or consistently overestimate the value you want to know.;

Random sampling: uses chance to select a sample.;

basic random sampling method: is a simple random sample (SRS). An SRS gives every possible sample of a given size the same chance to be chosen Choose an SRS by labeling the members of the population and using slips of paper, random digits, or technology to select the sample.;

stratified random sample: divide the population into strata, groups of individuals that are similar in some way that might affect their responses. Then choose a separate SRS from each stratum and combine these SRSs to form the sample. When strata are "similar within but different between," stratified random samples tend to give more precise estimates of unknown population values than simple random samples.;

cluster sample: divide the population into groups of individuals that are located near each other, called clusters. Randomly select some of these clusters. All the individuals in the chosen clusters are included in the sample. Ideally, clusters are "different within but similar between." Cluster sampling saves time and money by collecting data from entire groups of individuals that are close together.;

Random sampling helps: avoid bias in choosing a sample. Bias can still occur in the sampling process due to undercoverage, which happens when some members of the population cannot be chosen.;

Voluntary Response Sample: Consists of people who choose themselves by responding to a general invitation. Occurs when a sample is composed of volunteers who may differ from individuals who don’t choose to volunteer;

nonresponse: when people can't be contacted or refuse to answer.;
response bias: Incorrect answers by respondents can lead to this bias.;

Principles of Experimental Design: Comparison, Random Assignment, Replication, Control;

Comparison: of at least two treatment groups;

Random Assignment: of exxperimental units to treatment;

Replication: Many experimental units in each treatment group;

Control: Of confounding variables;

Experimental Units: the individuals/subjects (person, animal, plant, virus, particle, etc) that are assigned to different treatments. (In hiring study- science lab faculty);

Explanatory Variable: the variable that is purposefully manipulated. This is also known as the factor. (In hiring study- applicant name);

Treatments: the different levels of the explanatory variable in the experiment. (In hiring study- Jennifer/John);

Response variable: the measured experiment outcome that is compared between treatment groups. (In hiring study- “hireability” rating and salary estimate);

Statistically Significant: percentage should be 5% or less to be significant;

population: the entire group of individuals we want information about;

Sample: a subset of individuals in the population from which we actually collect data;

Bias: a study flaw that leads to unrepresentative and/or inaccurate estimates. ;

Undercoverage: When part of the population has a reduced chance of being included in a sample which leads to bias;

Response Bias: Eliciting a response based on who you are talking to;

Nonresponse: When individuals chosen for a sample don’t respond. This can lead to bias if these individuals differ from respondents. ;

Question wording bias: When survey questions are confusing or leading;

Self reported response bias: When individuals inaccurately report their own traits;

Simple Random Sample (SRS): Of size n is chosen in such a way that every group of n individuals in the population has an equal chance to be selected as the sample;

Stratified Random Sample: To get a stratified random sample start by classifying the population into groups of similar individuals, called strata. Then choose a separate SRS in each stratum and combine these SRSs to form the sample. Similar within, different between groups ;

Cluster Sample: To get a cluster sample, start by classifying the population into groups of individuals that are located near each other, called clusters. Then choose an SRS of the clusters. All individuals in the chosen clusters are included in the sample. Different within, similar between groups;

Systematic Random Sample: To get a systematic random sample put the members of the population in some order. Select a starting point at random, and every nth member is selected to be in the sample. ;

Confounding variables: Provide alternative explanations for trends between explanatory and response variables;