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Statistics AP > Unit 4 > Flashcards

Unit 4 Flashcards

1
Q

Population

A

The entire group of individuals we want information about

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2
Q

Census

A

Collects data from every individual in the population

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3
Q

Sample

A

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

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4
Q

Convenience sample

A

Choosing individuals from the population who are easy to reach
-Often produces unrepresentative data

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5
Q

Bias

A

The design of a statistical study shows bias if it would consistently underestimate or consistently overestimate
-Convience and voluntary sampling

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6
Q

Voluntary response sample

A

Consits of people who choose themselves by repsonding to a general invitation

  • Usually representitive of some large, population of interest
  • Attracts people who feel strongly about an opinion and often share the same opinion
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7
Q

Random sampling

A

Involves using a chance process to determine which members of a populaiton are included in the samples

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8
Q

Simple Random sample

A

SRS of size n is chosen in such a way that every group of n individuals (combinations) in a population has an equal chance of being selected as the sample
-GIves each member and combo of members equal chance of being included/selected

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9
Q

Choosing a SRS with tech

A

Step 1: label. Give each individual in the population a distinct numerical label from 1 to N (population size)
Step 2: Randomize. Use a random number generator to obtain n (sample size) different intergerns from 1 to N

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10
Q

Choosing an SRS with Table D

A

Step 1: Label. Give each member of the population a numerical label with the same number of digigts. Try to use as few digits as possible
Step 2: Randomize. Read consecutive groups of digits of the appropriate length from L to R across a line in table d. Don’t use any digits outside populaiton size and don’t use repeats. Stop when you have n individuals
-All labels of the same length have the same chance to be chosen

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11
Q

Stratified random sample

A
  • To get a stratified random sample, start by classifiying the population into groups of similar individuals, called strata. Then choose a separate SRS i each stratum and combine these SRSs to form the sample
  • Works best when the indivudals in each stratum are similar with respect to what is being measured and when there are large differences between strata
  • SImilar within and dif between. Gives a more precise estimate than simple random samples of the same size
  • less variability/deviation in the stratified graph
  • Want each stratum to contain similar individuals and for there to be a large difference between strata
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12
Q

Cluster sample

A
  • To get one,s tart by classifuing the population into groups of individuals that are located near each other called clusters. Then choose an SRS of the clusters. ALl individuals in the chosen clusters are included in the sample
  • Sometimes used to save money and time
  • Sometimes people take and SRS of the cluster rather and survey all of the cluster
  • Don’t offer the statistical advantage of better inforation about the population like stratified samples do
  • Want each cluster to look like the population, but on a smaller size
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13
Q

Multistage sampling

A

Combines stratified and cluster sampling

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14
Q

Inference

A
  • Infer about the population from what we know about the sample
  • Inference from convience or voluntary samples would be misleading bc method of sampling is biased
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15
Q

Random sampling

A
  • Rely on it to avoid bias in choosing a sample
  • Unlikley that the results will be the same as the entire population
  • Properly designed samples avoid systemic bias, but their results are rarely correct and we expect hem to vary from sample to sample
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16
Q

Why do we use Random sampling

A
  • We can say how likely it is that sample results are close to the results of the population
  • The laws of probably allow trustworthy inferences about the population
  • Results come with “margin of error”
17
Q

Do larger or smaller random samples give better info abt the the pop

A

Larger
-By having a very large random sample, you can be confident that the sample result is very close to the truth about the population

18
Q

Sampling frame

A

The list of individuals that a sample will be made up of

19
Q

Undercoverage

A

Occurs when some members of the population cannot be chosen in a sample
-Sample survey misses homeless people

20
Q

Nonresponse

A

Occurs when an individual chosen for the sample can’t be contact or refuses to participate

  • often exceeds 50%
  • If the people who do respond respond differently than those who don’t bias results
  • Don’t confuse with “voluntary sample”
21
Q

Nonresponse vs voluntary response

A

-Nonresponse can occur only after a sample has been sleected. In voluntary repsonse- every individual has opted to take part so there won’t be non response

22
Q

Response bias

A
  • Lieing or answers incorrectly
  • Gender, race, ethnicity can effect responses and create a systemic pattern of inaccuracy
  • Wording and clarity of questions
23
Q

Observational study

A

Observes individuals and measures variables of interest but does not attempt to influence the responses
-Goal is to describe some group or situation, to compare groups or examine relationships between variables

24
Q

Experiment

A

Deliberately imposes some treatment on indviduals to measure their responses
- determine whether a treatment causes a change in response

25
Q

What type of study (experiment or observational) should you use to understand cause and effect

A

Experiment is the only source of fully convincing data

26
Q

Confounding variable

A

Variable that results in confounding

27
Q

Counfounding

A

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

28
Q

Treatment

A

Specific treatment 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

29
Q

Experimental units

A

The smallest collection of individuals to which treatments are applied. When the units are human beings, they are called subjects

30
Q

Why choose experiments over studies

A

Experiments can give good evidence of causation while studies cannot

31
Q

Factors

A

Aka explanatory variables

32
Q

Level

A

A specific value of each factor

33
Q

Random assignment

A

Means that experimental units are assigned to treatments using a chance process
-Eliminates bias and confounding variables and ensures the effect of other variables are spread equally among each group

34
Q

Principals for designing experiments

A
  1. Comparison- design compares 2+ variables
  2. Random assignment- chance assigns experimental units to treatments
  3. Control- keep other variables that might effect response the same
  4. Replication- use enough experimental units in each group so that any differences in the effects of the treatments can be distinguished from chance differences between the groups
35
Q

How do the dotplots of experiments with controlled variables compare to the dotplots of experiments without controlled variables

A
  • Controlling variables helps reduce the variability of the data
  • Dotplots with controlling have shorter ranges and are more scrunched up
36
Q

Completely randomized design

A

The units are assigned to the treatments completely by chance

  • Does not require each treatment to be assigned an equal number of subjects
  • If forced =, not completely randomized
37
Q

Control group

A

Used to provide a baseline for comparing the effects of other treatments

  • many recieve no treatment
  • some receive treatment (ex: comparing an and a new drug)
  • some groups have no control groups-> comparing effects of several treatments to see which one works better
38
Q

Placebo effect

A

Response to dummy treatment

Statistics AP (1 decks)

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