Stats Test 1 Flashcards
What is Statistics?
- science of extracting meaning from data
- art of persuading the universe to divulge information about
itself - methodology for using data to answer questions in the
presence of variation
Dogma of Statistics
Variation -> Uncertainty -> Dealing with and understanding uncertainty to extrapolate meaning
Process of Statistical Problem Solving
Collect -> Summarize -> Interpret (data)
Population
Entire group of individuals
Sample
Subgroup of the Population
Population fact
Parameter
Sample fact
statistic
Convenience sampling
Select people in the most convienient way
Volunteer Response Sampling
Individuals select themselves
Quota Sampling
Force the sample to meet specified quotas
Probability Sampling Designs
simple random sampling
cluster sampling
stratified random sampling
multistage sampling
Cluster Sampling
blocks are similar to population
random sample of clusters is taken
all individuals in the selected clusters are included in the
sample
Stratified Random Sample
classify population into groups (strata) that are different
from each other
individuals within a group (stratum) share a similar characteristic
select SRS from every group
combine SRS’s
Multistage Sample
(Lightning)
SRS of states
for selected states, SRS’s of counties
for selected counties, SRS’s of people
combine SRS’s of people
Continuous Quantitative variable
Any number
Discrete Quantitative variable
Only whole numbers
Discrete Categorical variable
Only categories
Observational Studies
individuals choose which treatment to receive or naturally
belong to one of the treatment groups
lurking variables that influence choice confounded with
treatments
passive data collection: observing, measuring, counting,
subjects are undisturbed
media often improperly attribute cause-effect conclusions
to these
Experiment
a study design where treatments are imposed on subjects before observing response (manipulations, interventions)
Response variable
characteristic measured on each subject; outcome of interest
Explanatory variable
used to predict or explain changes in the response variable
Factor
planned explanatory variable (umbrella term for all treatments)
Treatment
the condition or conditions applied to a subject or individual in an experiment
Principles of Valid Experiments
- Control/Comparison
- Randomization
- Replication
- (Double-Blinding)
Hawthorne Effect
phenomenon where people in an
experiment behave differently from how they would
normally behave; attention/observation bias
Randomized Controlled Experiment (RCE)
Subjects assigned to treatments such that each subject has an
equal chance of being assigned to any possible treatment
(typically with the same number of subjects per treatment)
Randomized Block Design (RBD)
An experimental design where the random assignment of
individuals to treatments is carried out separately within
each block.
Matched Pairs
Special case of randomized block designs
Twins: each receiving a treatment
Two treatments on each individual
Measurements before and after treatment on each
individual
What can you extrapolate from a well-designed experiment that you cannot extrapolate from a study?
A cause and effect relationship
What is a distribution of a random variable?
A list of possible values of a variable together with how often each value occurs
Why do we randomize in experiments
To eliminate bias associated with lurking variables
Why do we use replication in experiments?
To remove extraneous variation from the experiment error
Probability samples are samples selected in such a at that
all samples of size n have the same chance of being selected
Jane, a student at BYU, decides to study opinions of BYU students concerning grading in religion classes. She obtains a roll from every religion class and randomly selects five students on each roll. This is an example of
Stratified sampling
The explanatory variable is
factor status
Empirical rule
68 95 99.7
1sd 2sd 3sd
