AP Stats Unit 3 vocab

Observational Study

Observational Study

no treatment, can’t show causation

treatments

what is done/not done to experimental units

Experimental units

who/what treatment is imposed on

Factors

explanatory variables

levels

value of factors

control group

used to provide baseline data for comparisonb

blinding

when subjects (single-blind) and/or experimenter (double) who interact are unaware of what treatment is given

placebo effect

when a fake treatment (placebo) works.

key principles of experiments

1. Comparison ( 2+ treatments)

2. Random Assignment

3. Control ( keep all other variables beside treatments constant)

   1. Replication ( use enough expSt units to distinguish difference )

Statistically Significant

if the likelihood of it occurring by chance alone is less than 5%. (larger sample sizes decrease the variability of estimates and make it easier to determine statistical significance

How to Describe an Experiment

1. State subjects if not yet listed

2. Justify blocks/pairs similar if needed - choose one

3. Randomly assign to treatments stating how many go to each

4. Repeat if needed for other blocks

   1. State what you are comparing in context

Random sampling

allows us to generalize the results to the population from which we sampled

Random assignment

allows us to say a treatment causes changes in the response variables

population

the pool of individuals from which a statistical sample is drawn for a study

sample

the specific group that you will collect data from

sample

the specific group that you will collect data from

parameter of interest

a statistical value that gives you info about the research sample or population being studied

Simple random sample

choose a group from the population so that every individual and every group of individuals is equally likely to be chosen.

Pro: removes all hint of bias

Con: difficulty gaining access to a list of a larger population, time, costs, and that bias can still occur under certain circumstances

Cluster sample

splits population into groups that are different. Randomly selects groups + samples everyone in the group. Each cluster is a mini representation of the entire population.

Pro: requires fewer resources and is more feasible

Con: causes biased samples and high sampling error

Stratified sample

split the population into groups that are the same. Chose an SRS from each strata

Pros: It ensures each subgroup within the population receives proper representation within the sample

Con: it is unusable when researchers cannot confidently classify every member of the populationSys

Systematic Sample

chose a random starting point. Use equal intervals to move to the next individual until you have as many as you need

Pro: eliminates the phenomenon of clustered selection and a low probability of contaminating data

Con: over/underrepresentation of particular patterns and a greater risk of data manipulation

Convenience sample

Selects individuals from the population who are easy to reach.

Pro: cheap, efficient, and simple to implement

Con: produces unrepresentative data

Voluntary response

People choose whether or not they want to be involved

Pro: an inexpensive way to conduct a study as data is very easy to gather

Con: the researcher has no control over the makeup of the sample. Untrustworthy

Observational Study

observes individuals + measures variables of interest; doesn’t attempt to influence the responses

Pro: provide critical descriptive data and information on long-term efficacy and safety that clinical trials cannot provide, at generally much less expense

Con: lower standard of evidence than experimental studies, are more prone to bias and confounding, and cannot be used to demonstrate causality

sampling bias

the design of a study shows bias if it’s likely to under/overestimate the value you want to know. It’s going to systematically fail due to a faulty design, resulting in the over/underestimations

sampling variability

the idea that different random samples of the same size from the population produce different estimate. Reduce sampling variability by sample size, what we call error.

confounding

occurs when 2 variables are associate in such a way that their effects on a response variable cannot be distinguished from each other

experiment

deliberately imposes treatments on individuals to measure their response; show causation

completely randomized design

an experiment done without blocking

randomized block design

separate subjects into blocks and then randomly assign treatments within each blockb

block

group of experimental units that are similar in some way that could be a cause of confounding. used to remove the effects of confounding

matched pairs design

a common experimental design for comparing 2 treatments that uses blocks of size 2

scope of inference

random sample (inference about population) + random assignment (inference about cause/effect)

undercoverage

when some members of a population cannot or are less likely to be included in a sample

nonresponse

when an individual is part of a survey but chooses not to respond or cannot be reached. this is different than voluntary response where people put themselves in the sample

response bias

pattern of inaccurate results ( wording of a questions, interviewer, lying, etc.)

explanatory variable

helps explain/predict response (on the x-axis)

response variable

outcome being measured ( on the y-axis )