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Statistics
the study of how to collect, organize, analyze, and interpret numerical information from data

Individuals
the people or objects included in the study

Variable
a characteristic of the individual to be measured or observed

quantitive variable
has a value or numerical measurement for which operations such as addition or averaging make sense

qualitative variable
describes and individual by placing the individual into a category or group, such as male or female

population data
the data are from every individual of interest.

sample data
the data are from only some of the individuals of interest

population parameter
 a numerical measure that describes and aspect of a population.
 Eg. The proportion of males in the population of al climbers who have conquered Mt. Everest

sample statistic
 a numerical measure that describes an aspect of a sample
 this is an example of a statistic
 sample statistics can vary from sample to sample

levels of measurement
 another way to classify data other than qual or quant
 levels indicate the appropriate type of arithamatic

nominal level
 data that consist of names, labels, or categories.
 no implied criteria by which data can me ordered from smallest to largest

ordinal level
applies to data that can be arranged in order. However differences between data values either cannot be determined or are meaningless

interval level
applies to data that can be arranged in order. in addition, differences between data values are meaningful.

ratio level
 applies to data that can be arranged in order. In addition, both differences between data values and ratios of data values are meaningful.
 Data at the ratio level have a true zero.

descriptive statistics
involves methods of organizing, picturing, and summarizing information from samples or populations

Inferential statistics
involves methods of using information from a sample to draw conclusions regarding the population

simple random sample
of n measurements from a population is a subset of the population selected in such a manner that every sample of size n from the population has an equal chance of being selected.

simulation
a numerical facsimile or representation of a realworld phenomenon

sampling with replacement
 in some software this means that although a number is selected fro the sample, it is not removed from the population
 the same number may be selected for the sample more than once

random sampling
use a simple random sample from the entire population

stratified sampling
 divide the entire population into distinct subgroups called strata.
 The strata are based on a specific characteristic such as age, income, etc.
 All members of a stratum share the specific characteristic.
 Draw random samples from each stratum.

systematic sampling
number all members of the population sequentially. Then, from a starting point selected at random, include every kth member of the population in the sample.

cluster sampling
divide the entire population into preexisting segments or clusters. The clusters are often geographic. Make a random selection of clusters. Include every member of each selected cluster in the sample.

Multistage sampling
Use a variety of sampling methods to create successively smaller groups at each stage. The final sample consists of clusters.

Conveinence sampling
 Create a sample by using data from population members that are readily available
 risk of being severely biased

sampling frame
 a list of individuals from which a sample is actually selected
 idealy the entire population

undercoverage
results from omitting population members from tjhe sample frame

sampling error
 the difference between measurements from a sample and corresponding measurements from the respective population. it is caused by the fact that the sample does not perfectly represent the population
 these do not represent mistakes
 simply the consequences of using samples instead of populations

nonsampling error
the result of poor sample design, sloppy data collection, faulty measuring instruments, bias in questionnaires, and so on

census
measurements or observations from the entire population are used

sample
measurements or observations from part of the population are used.

observational study
observations and measurements of individuals are conducted in a way that doesn't change the response ofr the variable being measured

experiment
a treatment is deliberately imposed on the individuals in order to observe a possible change in the response or variable being measured

placebo effect
occurs when a subject receives no treatment but (incorrectly)believes he or she is in fact receiving treatment and responds favorably.

control group
 the group that receives dummy treatment, enabling researchers to control for the placebo effect
 used to account for influence or other known or unknown variables that might be an underlying cause of a change in response in the experimental group

treatment group
they get the real treatment

completely randomized experiment
one in which a random process is used to assign each individual to one of the treatments.

block
a group of individual sharing some common features that might affect the treatment

randomized block experiment
individuals are first sorted into blocks, and then a random process is used to assign each individual in the block to one of the treatments

randomization
used to assign individuals to the two treatment groups. this helps prevent bias in selecting members for each group

replication
repeat the experiment on many paitents reduces the possibility that the differences in pain relief for the two groups occurred by chance alone

double blind
 neither the individuals or the observers know which subjects are receiving the treatment
 help control for suibtle biases that a doctor might pass on to a patient

surveying
a common means to gather data about people by asking them questions

Likert scale
a scale like strongly disagree to strongly agree

nonresponse
 individuals either cannot bve contacted or refuse to participate.
 nonresponse can result in significant undercoverage of a population

voluntary response
individuals with strong feelings about a subject are more likely than others to respond.

lurking variable
one for which no data have been collected but that nevertheless has influence on other variables in the study

confounded variable
 for two variables when the effects of one cannot be distinguished from the effects of the other.
 they may be part of the study, or they may be outside lurking variables

generalizing results
to generalize the findings of a study to a situation of wider scope than that of the actual data setting

study sponsor
a concern for studys as subtle bias may be introduced

