AP Stats chapter 10-11 quiz

Simple Random Sample (SRS)

a statistical sampling method where every member of a population has an equal chance of being selected for a study, and every possible sample of a given size has an equal chance of being chosen

Stratified Random Sample

a research technique that divides a population into distinct subgroups, or strata, before randomly selecting a sample from each subgroup

Cluster Sampling

a method where a large population is divided into groups (clusters), and then a random sample of these clusters is selected, with all or a random portion of the individuals from the selected clusters being studied

Multistage Samples

created by selecting sample units in two or more sequential stages from a population, often using a combination of cluster and stratified sampling techniques

Systematic Sampling

a probability sampling technique where researchers select members from an ordered population at a regular interval, called the sampling interval

Cluster Systematic Sample

a combination of cluster sampling and systematic sampling applied in a multi-stage process. First, the population is divided into natural, non-overlapping groups called clusters. Then, some of these clusters are selected, not randomly, but systematically, by applying a fixed interval or pattern. Finally, a sample is drawn within the selected clusters using a systematic approach

Voluntary Response Bias

occurs when a survey or poll allows individuals to choose whether or not to participate, leading to a sample that is skewed and unrepresentative of the larger population

Convenience Sampling

a non-random sampling method where researchers select participants based on their immediate availability and ease of access, rather than by random selection

Undercoverage

occurs in sampling when certain members of the target population are excluded from the sampling frame, leading to their underrepresentation or complete omission from the sample

Nonresponse Bias

a type of systematic error that occurs when individuals who do not participate in a study or survey differ meaningfully from those who do.

Response Bias

is a broad term for the many factors that can cause a participant in a survey, interview, or study to respond inaccurately or untruthfully. These inaccurate responses can lead to flawed data, which can seriously impact research validity and business decisions

Self Response (Type of Response Bias)

Self-response, in the context of response bias, refers to the phenomenon where individuals participating in surveys or interviews provide inaccurate or untruthful answers to questions, particularly when those questions require self-reporting of behaviors, attitudes, or experiences. This can occur intentionally or unintentionally and significantly impacts the validity and reliability of research data.

Questioner (type of Response Bias)

Interviewer bias is the response bias caused by the questioner or interviewer, where their characteristics, actions, or presence influence how a respondent answers questions. This can lead to skewed or inaccurate data, particularly in face-to-face or telephone interviews. 

Random

Simulation

a model that mimics a real-world system or process to provide insights, test scenarios, or train individuals without the risks of a real-world situation

Trial

the statistical analysis of trials, which are the individual experiments or observations within a larger study

Components

the core elements that represent and behave like aspects of a real-world system

population

quantify a population's characteristics, such as its size, density, distribution, and demographics, to understand societal and economic trends

sample

numerical values calculated from a subset (a sample) of a larger group (a population) to describe that sample and infer characteristics about the entire population

biased

Biased statistics are systematic errors in data collection, analysis, or interpretation that cause results to consistently deviate from the true value of what is being measured

Benefit of Randomizing

eliminates selection bias and confounding by ensuring that both known and unknown variables are balanced across treatment groups

Sample Size

the number of individuals or observations included in a study to represent a larger population

Census

Using the whole population of the topic

Population Parameter

a fixed numerical value that describes a characteristic of an entire populatio

Sample Statistic

a descriptive numerical measure (like a mean or proportion) calculated from a subset of a larger population, used to make inferences about the entire population's characteristics