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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
# Stats 101 order of questions
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?
26
# Stats 101 nonresponse
someone who refuses to respond to a survey question or is unavailable
27
# Stats 101 Missing data
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
28
# Stats 101 precise numbers
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.
29
# Stats 101 percentages
some studies have unclear or misleading percentages | ex: references to percentages that exceed 100%
30
# Stats 101 percentage of
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
31
# Stats 101 Fraction → Percentage
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
# Stats 101 Decimal → Percent
Multiply by 100. | .25 × 100 = 25%
33
# Stats 101 Percentage → Decimal
85% = 85/100 = .85
34
# Stats 101 parameter
a numerical measurement that describes some characteristic of a population
35
# Stats 101 statistic
a numerical measurement describing some characteristic of a sample.
36
# Stats 101 2 statistics definitions
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
# Stats 101 2 statistics definitions
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
# Stats 101 quantitative data
data which consists of numbers representing counts or measurements
39
# Stats 101 categorical data
aka qualitative or attribute data | names or labels that are not numbers representing counts and measurements
40
# Stats 101 discrete data
result when the data values are quantitative and the number of values is finite or countable. use the word "fewer"
41
# Stats 101 continuous (numerical) data
result from infinitely many possible quantitative values, where the collection of values is not countable use the word "less"
42
# Stats 101 nominal level of measurement
data that consists of names, labels and categories only. data cannot be ranked. ex: eye colors
43
# Stats 101 ordinal level of measurement
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
# Stats 101 interval level of measurement
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
# Stats 101 ratio level of measurement
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
# Stats 101 observational study
observe and measure certain characteristics, we don't attempt to modify the subjects being studied.
47
# Stats 101 experiment
some treatment is applied and it's effects on subjects are observed. subjects are called "experimental units."
48
# Stats 101 lurking variable
affects the variables included in the study, but is not included in the study.
49
# Stats 101 simple random sample
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
# Stats 101 random sampling
each member of the population has an equal chance of being selected: computers used to generate random phone numbers
51
# Stats 101 systematic sampling
select some starting point, and then select every kth element in the population
52
# Stats 101 Convenience sampling
Use results easiest to get
53
# Stats 101 Stratified Sampling
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
# Stats 101 cluster sampling
divide the population into sections or clusters then randomly select some of those clusters, and then choose all members from those selected clusters. 
55
# Stats 101 multistage sampling
pollsters select a sample in different stages and each stage might use different methods of sampling.
56
# Stats 101 cross-sectional study
data are observed, measured and collected in one point in time rather than a period of time.
57
# Stats 101 retrospective study
aka case control study | data are collected from a past time period by going back in time (observing records, interviews etc.)
58
# Stats 101 prospective study
aka longitudinal or cohort study | data are collected in the future from groups that share common factors.
59
# Stats 101 randomization
used when subjects are assigned to different groups through a process of random selectionf
60
# Stats 101 Replication
repetition of the experiment on more than one subject.
61
# Stats 101 blinding
when subject doesn't know if she is receiving the treatment or the placebo. 
62
# Stats 101 placebo effect
when an untreated subject reports an improvement in symptoms
63
# Stats 101 Double-blind
neither the subjects nor the experimenter know what group the subject is in
64
# Stats 101 confounding
occurs when the investigators are not able to distinguish among the effects of different factors 
65
# Stats 101 completely randomized experimental design
assign subjects to different treatments groups by a process of random selection
66
# Stats 101 block
a group of subjects that are similar
67
# Stats 101 randomized block design
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
# Stats 101 matched pairs design
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
# Stats 101 Rigorously Controlled Design
Carefully assign subjects to different treatment groups, so that those given each treatment are similar in ways that are important to the experiment.
70
# Stats 101 sampling error
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
# Stats 101 nonsampling error
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
# Stats 101 nonrandom sampling error
the result of using a sampling method that is not random, such as using a convenience sample or a voluntary response sample.
73
# Stats 101 which of the following is not a level of measurement: | ordinal, nominal, ratio, quantitative
Quantitative
74
# Stats 101 favorite films: choose the correct level of measurement: ratio, interval, Ordinal, nominal
Nominal
75
# Stats 101 When a Limo is randomly selected, it is found to have an engine with 116 hp
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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