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the study of how to collect, organize, analyze, and interpret numerical information from data
the people or objects included in the study
a characteristic of the individual to be measured or observed
has a value or numerical measurement for which operations such as addition or averaging make sense
describes and individual by placing the individual into a category or group, such as male or female
the data are from every individual of interest.
the data are from only some of the individuals of interest
- 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
- 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
- data that consist of names, labels, or categories.
- no implied criteria by which data can me ordered from smallest to largest
applies to data that can be arranged in order. However differences between data values either cannot be determined or are meaningless
applies to data that can be arranged in order. in addition, differences between data values are meaningful.
- 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.
involves methods of organizing, picturing, and summarizing information from samples or populations
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.
a numerical facsimile or representation of a real-world 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
use a simple random sample from the entire population
- 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.
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.
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.
Use a variety of sampling methods to create successively smaller groups at each stage. The final sample consists of clusters.
- Create a sample by using data from population members that are readily available
- risk of being severely biased
- a list of individuals from which a sample is actually selected
- idealy the entire population
results from omitting population members from tjhe sample frame
- 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
the result of poor sample design, sloppy data collection, faulty measuring instruments, bias in questionnaires, and so on
measurements or observations from the entire population are used
measurements or observations from part of the population are used.
observations and measurements of individuals are conducted in a way that doesn't change the response ofr the variable being measured
a treatment is deliberately imposed on the individuals in order to observe a possible change in the response or variable being measured
occurs when a subject receives no treatment but (incorrectly)believes he or she is in fact receiving treatment and responds favorably.
- 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
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.
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
used to assign individuals to the two treatment groups. this helps prevent bias in selecting members for each group
repeat the experiment on many paitents reduces the possibility that the differences in pain relief for the two groups occurred by chance alone
- 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
a common means to gather data about people by asking them questions
a scale like strongly disagree to strongly agree
- individuals either cannot bve contacted or refuse to participate.
- nonresponse can result in significant undercoverage of a population
individuals with strong feelings about a subject are more likely than others to respond.
one for which no data have been collected but that nevertheless has influence on other variables in the study
- 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
to generalize the findings of a study to a situation of wider scope than that of the actual data setting
a concern for studys as subtle bias may be introduced