AP Stat Unit 4 vocab

Population

In a statistical study is the entire group of individual we want information about

Leads to parameter -> true mean and treu standard deviation

Census

Collects data from everyone in a population

Sample

Is a subset of individuals in the population from which we actually collect data

Leads to statistics

Bias

Can be seen in a sample if it consistently over or under estimates a value

• non response bias (huge amount of people polled-little response), under coverage bias (only asked one section of population)

-convince sample and voluntary samples often show bias

Simple random sample

(Srs)- every group and individuals has an equal chance of being selected for the sample

Stratified random sample

Starts by classifying the population into groups or strata. then choosing a SRS from each group before combining all the SRS to form the sample (Statisticians make these samples)

Cluster sampling

Starts b classifying the population into pre-existing groups called clusters. The choosing a SRS of the clusters. All individuals in the cluster are chosen in the sample (these groups are pre-existing)

Systematic sampling

Follow a system where they ask every Kth person

Observational study

Observes individuals and measures variables of interest but does not attempt to influence the responses- cannot conclude cause and effect

-observational studies of the effect of an explanatory variable on a response variable often fail because of confounding between the explanatory variable and one or more other variables

Experiment

Deliberately imposes some treatment on individuals to measure their responses- can lead to cause and effect

Confounding

Occurs when two variables are associated in such a way that their effects on a response variables cannot be distinguished from each other

Treatment

Specific conditions applied to the individuals in an experiment- if an experiment has several explanatory variables, a treatment is a combination of specific values of these variables

Experimental unit

The smallest collection of individuals to which treatments are applied, when the unites are human beings, they often are called subjects

Explanatory variable

A variable that helps explain or influences changes in a response variable (independent variable)

Response variable

A variable that measures an outcome of a study (dependent variable)

comparison

Use a design that compares two or more treatments

Random assignment

Use chance to assign experiments units to treatments. Doing so helps create roughly equivalent groups of experimental units by balancing the effects of other variables among treatment groups

Control

Limit other variable that might affect the response the same for all groups

"In all other ways the two groups will be treated exactly the same, thus controlling other variables the best that we can"

Replication

Use enough experimental units in each groups so that any differences in the effects of the treatments can be distinguished from chance differences between groups

"Some replication occurred, but more research is recommended"

Completely random design

The treatments are assigned to all the experimental units completely by chance. Some experiments may include a control group that receives an inactive treatment (placebo)or an existing baseline treatment

Placebo effect

The response to a dummy treatment

Double blind experiment

Neither the subjects nor those who interact with them and measure the response variable know which treatment a subject receives- "ideal" but not always possible

Statistically significant

An over seven effect so large that it would rarely occur by chance- a statistically significant association in data from a well-designed experiment does imply causation

Block

A group of experimental units that are now before the experiment to be similar in some way that is expected to affect the response to the treatments

Randomized block design

The random assignment of experimental units to treatments is carried out separately within each block

Match pair design

A randomized blocked experiment in which each block consists of a matching pair of similar experimental units. Chance is used to detriment which unit in each pair gets each treatment. Sometimes a "pair" in a matched-pairs design consists of a single units that receives both treatments. Since the order of the treatments can influence he response, chance is used to determine which treatment is applied first to each unit

Random assignment

Can conclude cause and effect

Random selection

Can conclude generalization for the larger population