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Stats > intro to statistics > Flashcards

intro to statistics Flashcards

1
Q

Stats 101

Four Common Misleading Graph Methods

A
  1. Graphing an inappropriate statistic
  2. Omitting the zero on the relevant scale
  3. Manipulating the scale
  4. Two dimensions to represent one dimension
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2
Q

Stats 101

Voluntary Response Sample

A

When a large group of individuals is invited to respond and those that don’t are not counted. They respond because they have a high interest in the topic surveyed.

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3
Q

Stats 101

Random Selection

A

occurs when every member of population to which we would like to generalize our results has an equally likely chance of being chosen to participate in the study

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4
Q

Stats 101

Data

A

collections of observations, such as measurements, genders, or survey responses.

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5
Q

Stats 101

Datum

A

a single data value

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6
Q

Stats 101

Statistics

A

the science of planning studies and experiments; obtaining data; and then organizing, summarizing, presenting, analyzing and interpreting those data and then drawing conclusions based on them.

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7
Q

Stats 101

Population

A

the complete collection of all measurements or data that are being considered.

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8
Q

Stats 101

Census

A

the collection of data from every member of the population.

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9
Q

Stats 101

sample

A

a subcollection of members selected from a population

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10
Q

Stats 101

process in conducting statistical study

A

prepare
analyze

conclude

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11
Q

Stats 101

Context

A

what do the data mean?

What is the goal of the study

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12
Q

Stats 101

Source of the data

A

are the data from a source with a special interest so that there is pressure to obtain results that are favorable to the source?

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13
Q

Stats 101

sampling method

A

Were the data collected in a way that is unbiased, or were the data collected in a way that is biased. (such as a procedure in which respondents volunteer to participate)

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14
Q

Stats 101

Prepare

A
  1. context
  2. source of data
  3. sampling method
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15
Q

Stats 101

Analyze

A
  1. graph the data
  2. explore the data
  3. apply statistical methods
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16
Q

Stats 101

Explore the data

A

Are there any outliers (numbers very far away from almost all of the other data)?
What important statistics summarize the data (such as the mean and standard deviation )?

Did many selected subjects refuse to respond?

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17
Q

Stats 101

Apply statistic methods

A

use technology to obtain results

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18
Q

Stats 101

Conclude

A
  1. statistical significance
  • do the results have statistical significance?
  • Do the results have practical significance?
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19
Q

Stats 101

statistical significance

A

achieved in a study when a result is given that is not likely to occur by chance.

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20
Q

Stats 101

practical significance

A

the results have some meaningful and useful implications for the real world

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21
Q

Stats 101

misleading conclusions

A

can come from reported results
small samples

and other errors

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22
Q

Stats 101

reported results

A

subjects report their results rather than the surveyor taking measurements. (I lost 5 lbs - could be a lie.)

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23
Q

Stats 101

Small Samples

A

Conclusions should not be based on samples that are far too small.

Example: Basing a school suspension rate on a sample of only three students.

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24
Q

Stats 101

loaded questions

A

survey questions intentionally worded to elicit a desired response.

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25
Q

Stats 101

order of questions

A

survey questions are unintentionally loaded by the order of items being considered.
ex: “would you say traffic contributes more to air pollution than industry?
would you say industry contributes more to air pollution than traffic?

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26
Q

Stats 101

nonresponse

A

someone who refuses to respond to a survey question or is unavailable

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27
Q

Stats 101

Missing data

A

can dramatically affect results
- can be caused by random factors such as people dropping out

-special factors such as low income people refusing to admit their annual income

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28
Q

Stats 101

precise numbers

A

241,472,385 adults in U.S.
people assume that because the number is so precise, it must be accurate. that number is an estimate and would be better represented as 240 million adults.

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29
Q

Stats 101

percentages

A

some studies have unclear or misleading percentages

ex: references to percentages that exceed 100%

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30
Q

Stats 101

percentage of

A

to find the percentage of an amount, drop the % symbol and divide the percentage value by 100.
6% of 1,200 respondents

6⁄100 × 1,200 = 72

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31
Q

Stats 101

Fraction → Percentage

A

divide the denominator into the numerator to get an equivalent decimal number, then multiply by 100 to get percent.

3/4 = .75 → .75× 100 = 75%

32
Q

Stats 101

Decimal → Percent

A

Multiply by 100.

.25 × 100 = 25%

33
Q

Stats 101

Percentage → Decimal

A

85% = 85/100 = .85

34
Q

Stats 101

parameter

A

a numerical measurement that describes some characteristic of a population

35
Q

Stats 101

statistic

A

a numerical measurement describing some characteristic of a sample.

36
Q

Stats 101

2 statistics definitions

A
  1. two or more numerical measurements describing characteristics of samples.
  2. the science of planning studies and experiments; obtaining data; organizing; summarizing, presenting, analyzing and interpreting those data; and then drawing conclusions based on them.
37
Q

Stats 101

2 statistics definitions

A
  1. two or more numerical measurements describing characteristics of samples.
  2. the science of planning studies and experiments; obtaining data; organizing; summarizing, presenting, analyzing and interpreting those data; and then drawing conclusions based on them.
38
Q

Stats 101

quantitative data

A

data which consists of numbers representing counts or measurements

39
Q

Stats 101

categorical data

A

aka qualitative or attribute data

names or labels that are not numbers representing counts and measurements

40
Q

Stats 101

discrete data

A

result when the data values are quantitative and the number of values is finite or countable.
use the word “fewer”

41
Q

Stats 101

continuous (numerical) data

A

result from infinitely many possible quantitative values, where the collection of values is not countable
use the word “less”

42
Q

Stats 101

nominal level of measurement

A

data that consists of names, labels and categories only. data cannot be ranked.
ex: eye colors

43
Q

Stats 101

ordinal level of measurement

A

data that can be arranged in some order, but differences (obtained by subtraction) between data values either cannot be determined or are meaningless.
rank of colleges in U.S news & world report

44
Q

Stats 101

interval level of measurement

A

data that can be arranged in order, and the differences between data values can be found and are meaningful. data at this level do not have a natural zero starting point at which none of the quality is present. (such as time, because there is no year zero, or degrees in F)

45
Q

Stats 101

ratio level of measurement

A

data that can be arranged in order, differences can be found and they are meaningful, and there is a natural zero starting point (where zero indicates none of the quantity are present.) ex: height, length, distance, volume

46
Q

Stats 101

observational study

A

observe and measure certain characteristics, we don’t attempt to modify the subjects being studied.

47
Q

Stats 101

experiment

A

some treatment is applied and it’s effects on subjects are observed.
subjects are called “experimental units.”

48
Q

Stats 101

lurking variable

A

affects the variables included in the study, but is not included in the study.

49
Q

Stats 101

simple random sample

A

of n subjects is selected in such a way that every possible sample of the same size n has the same size chance of being chosen.

50
Q

Stats 101

random sampling

A

each member of the population has an equal chance of being selected: computers used to generate random phone numbers

51
Q

Stats 101

systematic sampling

A

select some starting point, and then select every kth element in the population

52
Q

Stats 101

Convenience sampling

A

Use results easiest to get

53
Q

Stats 101

Stratified Sampling

A

subdivide the population into at least two different subgroups (or strata) so that subjects within the same subgroup share the same characteristics (such as gender or age bracket,) then we draw a sample from each subgroup (or stratum).

54
Q

Stats 101

cluster sampling

A

divide the population into sections or clusters then randomly select some of those clusters, and then choose all members from those selected clusters.

55
Q

Stats 101

multistage sampling

A

pollsters select a sample in different stages and each stage might use different methods of sampling.

56
Q

Stats 101

cross-sectional study

A

data are observed, measured and collected in one point in time rather than a period of time.

57
Q

Stats 101

retrospective study

A

aka case control study

data are collected from a past time period by going back in time (observing records, interviews etc.)

58
Q

Stats 101

prospective study

A

aka longitudinal or cohort study

data are collected in the future from groups that share common factors.

59
Q

Stats 101

randomization

A

used when subjects are assigned to different groups through a process of random selectionf

60
Q

Stats 101

Replication

A

repetition of the experiment on more than one subject.

61
Q

Stats 101

blinding

A

when subject doesn’t know if she is receiving the treatment or the placebo.

62
Q

Stats 101

placebo effect

A

when an untreated subject reports an improvement in symptoms

63
Q

Stats 101

Double-blind

A

neither the subjects nor the experimenter know what group the subject is in

64
Q

Stats 101

confounding

A

occurs when the investigators are not able to distinguish among the effects of different factors

65
Q

Stats 101

completely randomized experimental design

A

assign subjects to different treatments groups by a process of random selection

66
Q

Stats 101

block

A

a group of subjects that are similar

67
Q

Stats 101

randomized block design

A

blocks differ in ways that might affect the outcome of the experiment
1. form blocks or groups with similar characteristics

2.randomly assign treatments to subjects within each block

68
Q

Stats 101

matched pairs design

A

compare two treatments groups (such as treatment and placebo) by using subjects that are matched in pairs that are somehow related or have similar characteristics examples:
Before/After

Twins

69
Q

Stats 101

Rigorously Controlled Design

A

Carefully assign subjects to different treatment groups, so that those given each treatment are similar in ways that are important to the experiment.

70
Q

Stats 101

sampling error

A

occurs when the sample has been selected with a random method, but there is discrepancy between a sample result and a true population result; such an error results from chance sample fluctuations

71
Q

Stats 101

nonsampling error

A

the result of human error, including such factors as wrong data entries, computer errors, questions with biased wording, false data provided by respondents, forming biased conclusions, or applying statistical methods that are not appropriate for the circumstances.

72
Q

Stats 101

nonrandom sampling error

A

the result of using a sampling method that is not random, such as using a convenience sample or a voluntary response sample.

73
Q

Stats 101

which of the following is not a level of measurement:

ordinal, nominal, ratio, quantitative

A

Quantitative

74
Q

Stats 101

favorite films:
choose the correct level of measurement:

ratio, interval, Ordinal, nominal

A

Nominal

75
Q

Stats 101

When a Limo is randomly selected, it is found to have an engine with 116 hp

A

It is from a continuous data set because the number of possible values is infinite and not countable.

Stats (15 decks)

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