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58 Cards in this Set

  • Front
  • Back
statistics
the art of collecting and organizing data as well as drawing inferences from the data
descriptive statistics
collecting
organizing
summarizing
describing
inferential statistics
drawing conclusions from the data
variable
a characteristic that takes on different values for different people and places and things (must vary)
quantitative variable
a variable that can be measured in the usual sense
qualitative variable
a characteristic that cannot be measured in the usual sense - categorical
population
the largest collection of entities for which we have an interest at a particular time
sample
a subset of the population
well representative sample
when the characteristics of the sample match the characteristics of the population
parameter
numerical value based on the population
statistic
a numerical value based on the sample
measurement
the assignment of numbers to objects or events according to a set of rules
four types of measurement
nominal scale
ordinal scale
interval scale
ratio scale
nominal scale
"naming observations" or classifying them into various mutually exclusive categories (ex: male/female)
ordinal scale
measurements can be ranked according to some criterion: low/average/high
interval scale
measurements can not only be ranked but the distance btwn the two is known - no true zero pt
ratio scale
measurements in which equality of ratios as well as equality of intervals is known - no true zero pt
methods for obtaining data
census
sampling
experimentation
census
get info from every element of the pop
two types of samples
nonprobability and probability
examples of nonprobability samples
sample of convenience
haphazard selection
judgment sampling
expert sampling
quota sampling
sample of convenience
samples already exist
haphazard selection
subjects casually met
judgment sampling
the researcher uses his/her own judgment in choosing the subjects
expert sampling
an "expert" picks the subjects
quota sampling
subjects are chosen so as to satisfy certain quotas (can also be probability sampling)
probability samples
when the sample is obtained by a chance process
types of probability samples
random samples
stratified samples
cluster samples
systematic sampling
random samples
all samples of the same size have equal probability of being selected
stratified samples
population is divided into subpops (strata) and a random sample is obtained from each strata
cluster samples
randomly selecting some of the strata and obtaining a random sample from the chosen strata (subjects are naturally clustered together)
systematic sampling
choose a random starting pt from a list of subjs and then select every nth subject on the list
selection bias
when exclude a specific characteristic or segment of the population
response bias
when subjects respond incorrectly by lying or exaggerating or forgetting or not understanding the question (misleading question)
nonresponse bias
when the response rate is low
two "types" of experiments
observational study
designed experiment
observational study
simply comparing two or more groups - only shows association
designed experiment
- control
- randomization (div subjs into groups)
- replication
treatment group
the group that receives the treatment
control group
group that does not receive the treatment
blind study
when the subjects don't know if they are receiving the treatment or placebo
double-blind study
neither the subjects nor the doctors know if subjects are in the treatment or control group
problems w/ experiments
placebo effect
hawthorne effect
rosenthall effect
placebo effect
when subjects improve because they believe they are receiving the treatment
rosenthall effect
when the researcher or experimenter unintentionally influences the outcome thru facial expressions or body lang or voice
hawthorne effect
when the outcome is affected because people change how they behave because they know they are being watched
frequency distribution
dot plot
grouped freq distribution
by age or grade
histogram
relative freq distribution
percent
measure of central tendency
conveys a "typical" value of the data set

ex: mean; median; mode
properties of mean
most commonly used
may not be a value in the data set
is not a resistance measure: mean will change when a value changes
median
value such that 50% of the data is smaller and 50% of the data is larger
find location of median
(n+1)/2
properties of median
may not be a value in the data set
is a resistance measure: median won't change when a value changes
resistance measure
a measure that is not affected by outliers
measures of dispersion
conveys info abt the amt of variability in the data:

range
variance
standard deviation
interquartile range
variance
measures the variability by comparing each data value to the mean

-- not a resistance measure
standard deviation
measures the variability in original units

-- not a resistance measure