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Statistics
the study of how to collect, organize, analyze, and interpret numerical information from data
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Individuals
the people or objects included in the study
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Variable
a characteristic of the individual to be measured or observed
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quantitive variable
has a value or numerical measurement for which operations such as addition or averaging make sense
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qualitative variable
describes and individual by placing the individual into a category or group, such as male or female
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population data
the data are from every individual of interest.
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sample data
the data are from only some of the individuals of interest
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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
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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
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levels of measurement
- another way to classify data other than qual or quant
- levels indicate the appropriate type of arithamatic
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nominal level
- data that consist of names, labels, or categories.
- no implied criteria by which data can me ordered from smallest to largest
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ordinal level
applies to data that can be arranged in order. However differences between data values either cannot be determined or are meaningless
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interval level
applies to data that can be arranged in order. in addition, differences between data values are meaningful.
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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.
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descriptive statistics
involves methods of organizing, picturing, and summarizing information from samples or populations
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Inferential statistics
involves methods of using information from a sample to draw conclusions regarding the population
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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.
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simulation
a numerical facsimile or representation of a real-world phenomenon
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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
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random sampling
use a simple random sample from the entire population
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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.
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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.
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cluster sampling
divide the entire population into pre-existing segments or clusters. The clusters are often geographic. Make a random selection of clusters. Include every member of each selected cluster in the sample.
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Multistage sampling
Use a variety of sampling methods to create successively smaller groups at each stage. The final sample consists of clusters.
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Conveinence sampling
- Create a sample by using data from population members that are readily available
- risk of being severely biased
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sampling frame
- a list of individuals from which a sample is actually selected
- idealy the entire population
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undercoverage
results from omitting population members from tjhe sample frame
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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
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nonsampling error
the result of poor sample design, sloppy data collection, faulty measuring instruments, bias in questionnaires, and so on
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census
measurements or observations from the entire population are used
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sample
measurements or observations from part of the population are used.
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observational study
observations and measurements of individuals are conducted in a way that doesn't change the response ofr the variable being measured
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experiment
a treatment is deliberately imposed on the individuals in order to observe a possible change in the response or variable being measured
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placebo effect
occurs when a subject receives no treatment but (incorrectly)believes he or she is in fact receiving treatment and responds favorably.
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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
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treatment group
they get the real treatment
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completely randomized experiment
one in which a random process is used to assign each individual to one of the treatments.
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block
a group of individual sharing some common features that might affect the treatment
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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
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randomization
used to assign individuals to the two treatment groups. this helps prevent bias in selecting members for each group
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replication
repeat the experiment on many paitents reduces the possibility that the differences in pain relief for the two groups occurred by chance alone
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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
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surveying
a common means to gather data about people by asking them questions
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Likert scale
a scale like strongly disagree to strongly agree
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nonresponse
- individuals either cannot bve contacted or refuse to participate.
- nonresponse can result in significant undercoverage of a population
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voluntary response
individuals with strong feelings about a subject are more likely than others to respond.
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lurking variable
one for which no data have been collected but that nevertheless has influence on other variables in the study
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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
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generalizing results
to generalize the findings of a study to a situation of wider scope than that of the actual data setting
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study sponsor
a concern for studys as subtle bias may be introduced
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