Chapter 5 Data Management Vocab

Front 1

Numerical Quantitative Data

Back 1

can be either continuous or discrete

Front 2

Continuous

Back 2

Any value which which can include decimals ex. time, weight, etc.

* in a histogram its bars are touching

Front 3

Discrete

Back 3

data which can only have values which are whole numbers

ex. how many windows are there in your house?

* bars on a histogram are spread apart

Front 4

Categorical qualitative data

Back 4

data which an be sorted into distinct groups or categories

Front 5

Ordinal Data

Back 5

data which can be split in to categories based on a qualitative rank ex. how was our service? Very good, good, ok, bad, very bad, idk

Front 6

Nominal Data

Back 6

Data which cant be ranked ex. eye colors of the people in your class- blue, green, brown, etc.

Front 7

Population

Back 7

The total of individuals being studied Ex. Canadian pop.

Front 8

Sample

Back 8

A group of items/ people selected from the population Ex. That spit thing at the science center

Front 9

Variability(in samples)

Back 9

Shows how samples are different from each other ^ similarity, decrease variability ~ pop rep by samples ^ accurately

Front 10

Treatment group

Back 10

Theparticipants who receive a treatment that is doing to be tracked

Front 11

Control group

Back 11

The participants who don’t receive the treatment being tracked Compared to the treatment group

Front 12

Bias

Back 12

When there is preference for or against and idea or response Can result from sampling technique or the way the data was collected

Front 13

Observational studies

Back 13

Researchers look at situations that are already occurring and try to draw results from it Ex. Comparing the health of people who do exercise vs. the people who don’t exercise

Front 14

Experimental Studies

Back 14

Researchers conduct and experiment and draw conclusions based off of the controls Ex. Making one group does rigorous exercise for 1 week and the other group continues with their normal lifestyle; their fitness would be measured at the end of the week. This type of experiment uses treatment vs control

Front 15

3things need to occur in order to determine a cause in the experiment:

Back 15

control, randomization, replication

Front 16

control

Back 16

Asmany things possible need to be controlled

Front 17

Randomization

Back 17

Participantsneed to be randomized so bias can be minimized in the groups

Front 18

Replication

Back 18

Researchersshould repeat experiments with groups of a similar make up for changes b/wgroups are easier to detect

Front 19

Primary source data

Back 19

Datacollected directly from the researcher that hasn’t been summarized ormanipulated

Front 20

Microdata

Back 20

Anindividual set of data about a single response

Front 21

Secondary source data

Back 21

Data manipulated or summarized by someone who didn’t collect the data themselves

Front 22

Aggregate data

Back 22

That that has been summarized do the microdata cant be picked out

Front 23

Response Bias

Back 23

Respondent changes their answer to avoid embarrassment/ to give the questioner the response they want Ex. Survey to 30 year olds: Can you read?

Front 24

Sampling Bias

Back 24

When the sample doesn't represent the population Ex. Asking old people at a retirement home if pension should be stopped

Front 25

Measurement Bias

Back 25

Using a leading question which influences the respondents answer ex. No one likes math, do you think we should buy more calculators? Using poor collection method ex. Providing a survey with limited answers

Front 26

Non-response bias

Back 26

When opinions of respondents differ in meaningful ways from those of non-respondents You can't control some surveys that are optional, not everyone may complete an email survey and the people who do might provide "over the top" feed back wit ha strong opinion which will sway your results into over estimating or underestimating the feelings of the pop.

Front 27

types of samples

Back 27

simple random, systematic, stratified, cluster, multiusage, convenience, voluntary

Front 28

Simple Random

Back 28

choosing a specific # of people randomly ex. pulling names from a hat

Front 29

Systematic Sampling

Back 29

ordering the population and choosing people at random intervals

Front 30

Stratified Sampling

Back 30

dividing the groups in to the same proportions as in the regular sample

Front 31

Cluster

Back 31

dividing the popualation into groups and then choosing a some of the groups and sampling each member in the groups

Front 32

Multiusage

Back 32

dividing the population in a hierarchy and then choosing a random sample at each level.

Front 33

Convenience

Back 33

choosing individuals from a pop that are easy to access. Results may have a lot of variability because of sample bias

Front 34

Voluntary

Back 34

participants choose to participate/ not to. The only people who respond have extreme result- either v. for or against