Stats - Collecting Data Flashcards
Convenience Sampling
Sampling in convenient places leaving you with not very accurate results
- ex. sampling about cafeteria food on the cafeteria line - the people on the line would give it a good rating
Bias
Systematically favoring a different outcome
- Sampling not randomly
- Choosing the people you sample which can lead to a different outcome because you can be choosing people that you think will give you a certain answer
Voluntary Response Sampling
Allows people to choose to be in the sample by responding to a general information
- Doesn’t require you to answer and it takes time so most people wont respond
- People who respond either really love it or really hate it - strongly opinionated people (usually negative responses)
- Someone who didn’t get their way
- Response bias
Random Sampling
Involving using a chance process to determine which members of a population are included in the sample
- Chosen with no bias/influence by the holder
Simple Random Sample (SRS)
Size of n is chosen in such a way that every group of n individuals in the population has an equal chance to be selected in the sample
N = population
n = Sample size (>1)
- Multiply to get the amount of combinations
Permutation
Used to see how many ways you are able to arrange something
-ex. 4 books on a shelf (4x3x2x1)
Census
Collects data from every individual in the population
Cluster Sampling
Selects a sample by randomly choosing clusters
Strata
Groups of individuals in a population who share characteristics thought to be associated with the variables being measured in the study
Systematic Sampling
Selects a sample from an ordered arrangement of the population by randomly selecting one of the first k individuals and choosing every kth individual there after
Random
everyone has an equal chance
Under coverage
When some members of the population are less likely to e chosen or cannot be chosen in a sample
- ex. having a survey on a Monday at 12 pm - excludes everyone at school or work
Nonresponse
When an individual chosen for the sample can’t be contacted or refuses to participate
- may not answer - makes it not random anymore
- gives you less accurate results
- leads to response bias
Response Bias
When there is a systematic pattern of inaccurate answers to a survey question
- someone obviously favoring a side
- agreeing just to be left alone
- leads to inaccurate results
- may not be intentional - they may not know the answer at the top of their head
- Someone may be messing around on the survey
Observational Study
Observes individuals and measures variables of interest but does not influence the response
- Data that is already collected to see the results (research)
- Not doing anything to affect the outcome or manipulate
Response Variable
Measures an outcome of a study
- The change
- Similar to an dependent variable
Explanatory Variable
May help or predict changes in a response variable
-What you think will cause the change
- Similar to an Independant variable
Confounding Variable
Occurs when two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other
- Also known as the lurking variable
- A variable that could possibly effect the outcome
Placebo
A treatment that doesn’t have any active ingredient, but is otherwise like other treatments
- A dummy treatment
- Control variable
Treatment
A specific conditions applied to the individuals in an experiment
Experimental Unit
Object to which a treatment is randomly assigned
Subjects
Often a name for experimental units that are human beings
Control Group
Control the confounding variables used to provide a baseline for comparing the effects of other treatments
Double-Blind
Neither the subjects nor those who interact with them and measure the response variable know which treatment a subject is receiving
Single-Blind
Either the subjects or the people who interact with them and measure the response variable don’t know which treatment a subject is receiving
Random Assignment
Means that experimental units are assigned to treatments using a change method
- Avoids bias (eliminates) - the person running it doesn’t choose
- Evens out the playing field
Control
Keeping other variables constant for all experimental groups
- Everything the same other than the treatment
Replication
Giving each treatment to enough experimental units so that a difference in the effects of the treatments can be distinguished
- Makes it more accurate
- Not by chance but by the treatment
Variation
Different sample from same population, you would get similar results but never the same
- More differences = larger sample sizes
- The larger your sample size the more accurate your results
Block
A group of experimental units that are known before the experiment to be similar in some way that is expected to affect the response to the treatments
Randomized block design
The random assignment of experimental units to treatments is carried out separately within each block
- Not completely random anymore
Matched Pairs Design
A common type of randomized block design for comparing two treatments
- Giving each subject both treatments
Sampling Variablity
Refers to the fact that different random samples of the same size from the same population produce different estimates
Sampling Distribution
Collection of all possible outcomes and averaging them
Statistically Significant
When the observed results of a study are too unusual to be explained by chance alone
