Studied by 2 people
voluntary response bias
the people responding feel super passionate about the subject (marriage)
Another common example can be seen on television. Some companies give the option to call-in and discuss a particular issue. Since calling in is completely voluntary, the company is very likely to hear from those individuals who feel strongly about the topic.
response bias
presenting the qs in a way to convince/influence the audience
the tendency for people not to be completely honest when asked about illegal behavior or unpopular beliefs.
Other things that might contribute to response bias are the appearance or behavior of the person asking the question, the group or organization conducting the study, and
non-response bias
Non-response bias, or population bias, typically occurs when people choose not to respond to a survey or sample, and their opinions do not get evaluated
making urself not available/lying
selection bias
selecting people who will tell you what you want to hear
under-coverage bias/survey
when a part of the population isn’t fully represented (ie. homeless pple in sf)
convenience bias
asking pple that r convenient for you (consensus of your neighborhood)
strata
groups of certain demographics/characteristics
each individual subgroup is called a stratum (the singular of strata)
Increasing the size of the sample, although possibly desirable for other reasons, does nothing to reduce bias if the method of selecting the sample is flawed or if the nonresponse rate remains high.
why is simple random sampling good
eliminates the possibility of bias
a sample chosen using a method that ensures that each different possible sample of the desired size has an equal chance of being the one chosen.
It is the selection process, not the final sample, which determines whether the sample is a simple random sample
basically j use random no generator
systematic sample
Systematic sampling is a procedure that can be used when it is possible to view the population of interest as consisting of a list or some other se-quential arrangement. A value k is specified (e.g., k ! 50 or k ! 200). Then one of the first k individuals is selected at random, after which every kth individual in the sequence is included in the sample. A sample selected in this way is called a 1 in k systematic sample.
For example, a sample of faculty members at a university might be selected from the faculty phone directory. One of the first k ! 20 faculty members listed could be selected at random, and then every 20th faculty member after that on the list would also be included in the sample. This would result in a 1 in 20 systematic sample.
The value of k for a 1 in k systematic sample is generally chosen to achieve a de-sired sample size. For example, in the faculty directory scenario just described, if therewere 900 faculty members at the university, the 1 in 20 systematic sample described would result in a sample size of 45. If a sample size of 100 was desired, a 1 in 9 sys-tematic sample could be used.
As long as there are no repeating patterns in the population list, systematic sampling works reasonably well. However, if there are such patterns, systematic sampling can result in an unrepresentative sample.
stratified sample
splitting population into strata (groups of interest) and randomly selecting people from those groups so everyone in the sample is represented appropriately
cluster random sample
creating clusters that are naturally occurring (like schools or communities) and randomly selects a few clusters to survey instead of randomly selecting individuals
fir this 2 work these clusters can’t be systematically different than the population and should ab equally represent all groups
4 types of observational study
list the steps to setting up an experiment
randomization
control
blocking
replication
response variable
the variable in an experiment or study that you measure or observe to see how it is affected by changes in other variables
Note 
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