AP Statistics: Unit 1

Treatment

A specific condition applied to individuals in an experiment

Statistically Significant

An observed effect so large that it would rarely occur by chance

Simple Random Sample (SRS)

Chosen in a way to where all individuals have an equal chance to be selected (Ex. 20 people out of 200 having an equal chance to be selected is an SRS)

Sample Survey

Collects data from a sample

Census

Collects data from every individual in a population

Matched Pairs Design

Compares two treatments by matching pairs of similar experimental units

Completely Randomized Design

Experimental units have an equal chance of receiving any treatment from experiments (Chance does not guarantee a completely randomized design)

Block

Group of experimental units known to be similar (Will also react similarly to treatments)

Clusters

Groups of individuals similar to each other (within a population) (Individuals sampled in a cluster aren't necessarily similar to each other; ideally, a cluster mimics a population on a smaller scale)

Strata

Groups of similar individuals (Chosen based on knowledge prior to taking sample)

Explanatory Variable

Help explain or predict changes in a response variable

Subjects

Human experimental units

Experiment

Imposes some treatment on individuals to measure their responses (Only source of fully convincing data)

Scope of Inference (For Random Samples)

Inference about population

Control (Principles of Experimental Design)

Keep other variables that might affect the response the same for all groups (Control is important because 1: You will be able to view the difference in response variables 2: It will reduce variability in the response variable) (Provides a baseline for comparing the effects of other treatments)

Random Sampling

Letting individuals in a population be chosen by chance (Uses a chance process to determine which members of a population are included in a sample)

Response Variable

Measures an outcome of a study

Double-Blind Experiment

Neither a subject nor researcher know who receives a treatment

Nonresponse vs Voluntary Response

Nonresponse occurs after a sample has been selected, individuals in a voluntary response opt to take part

Observational Study

Observes individuals and measures variables of interest but does not attempt to influence the responses (Poor way to gauge the effect that variables have)

Confounding

Occurs when 2+ correlated variables have an indistinguishable effect on response variables

Nonresponse (Sampling Issue)

Occurs when an individual cannot be contacted or refuses to participate (Can result in bias; Individuals cannot give data)

Undercoverage (Sampling Issue)

Occurs when members of a population cannot be chosen in a sample (Can result in bias; Not everyone will be able to give their data)

Randomized Block Design

Random assignment of experimental units to treatments is carried out separately within each block (Allows researcher to account for variation in the response that is due to the blocking variable. This makes it easier to determine if one treatment is more effective than another)

Experiment vs Observational Study

Researchers make no changes during observational studies, only during experiments

Placebo Effect

Response to a dummy treatment

Stratified Random Sample

Samples individuals by classifying the population into groups of similar individuals (called strata)

Cluster Sampling

Samples individuals into groups based on proximity (called clusters) (An SRS is then conducted using the clusters)

Convenience Sample (Bad Sampling)

Sampling from individuals who are easy to reach (Can result in bias; favors some outcomes over others meaning sample may not be representative)

Voluntary Response Sample (Bad Sampling)

Sampling from individuals who chose to respond (Can result in bias; voluntary sampling tends to attract people who feel strongly about an issue)

Experimental Units

Smallest collection of individuals to which treatments are applied

Strata vs Clusters

Sratas sample similar individuals, clusters sample individuals close to each other

Sample

Subset of population

Population

The entire group of individuals we want information about

Inference

The process of drawing conclusions about a population on the basis of sample data

Comparative Experiment (Principles of Experimental Design)

Use a design that compares two or more treatments (Produces data that can give good evidence for a cause-and-effect relationship between explanatory and response variables)

Replication (Principles of Experimental Design)

Using enough experimental units in each group so that differences in the effects can be distinguished (Control groups provide a baseline for comparing the effects of other treatments)

Bias

When data from a study is consistently underestimated or overestimated

Random Assignment (Principles of Experimental Design)

When experimental unitss are assigned treatments using a chance process

Single-Blind Experiment

When subjects don't know who is in the group receiving treatment and what they are receiving (Only researcher knows)