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1

Observational Study

no treatment, can’t show causation

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2

treatments

what is done/not done to experimental units

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3

Experimental units

who/what treatment is imposed on

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4

Factors

explanatory variables

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5

levels

value of factors

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6

control group

used to provide baseline data for comparisonb

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7

blinding

when subjects (single-blind) and/or experimenter (double) who interact are unaware of what treatment is given

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8

placebo effect

when a fake treatment (placebo) works.

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9

key principles of experiments

Comparison ( 2+ treatments)

Random Assignment

Control ( keep all other variables beside treatments constant)

Replication ( use enough expSt units to distinguish difference )

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10

Statistically Significant

if the likelihood of it occurring by chance alone is less than 5%. (larger sample sizes decrease the variability of estimates and make it easier to determine statistical significance

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11

How to Describe an Experiment

State subjects if not yet listed

Justify blocks/pairs similar if needed - choose one

Randomly assign to treatments stating how many go to each

Repeat if needed for other blocks

State what you are comparing in context

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12

Random sampling

allows us to generalize the results to the population from which we sampled

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13

Random assignment

allows us to say a treatment causes changes in the response variables

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14

population

the pool of individuals from which a statistical sample is drawn for a study

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15

sample

the specific group that you will collect data from

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16

sample

the specific group that you will collect data from

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17

parameter of interest

a statistical value that gives you info about the research sample or population being studied

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18

Simple random sample

choose a group from the population so that every individual and every group of individuals is equally likely to be chosen.

Pro: removes all hint of bias

Con: difficulty gaining access to a list of a larger population, time, costs, and that bias can still occur under certain circumstances

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19

Cluster sample

splits population into groups that are different. Randomly selects groups + samples everyone in the group. Each cluster is a mini representation of the entire population.

Pro: requires fewer resources and is more feasible

Con: causes biased samples and high sampling error

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20

Stratified sample

split the population into groups that are the same. Chose an SRS from each strata

Pros: It ensures each subgroup within the population receives proper representation within the sample

Con: it is unusable when researchers cannot confidently classify every member of the populationSys

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21

Systematic Sample

chose a random starting point. Use equal intervals to move to the next individual until you have as many as you need

Pro: eliminates the phenomenon of clustered selection and a low probability of contaminating data

Con: over/underrepresentation of particular patterns and a greater risk of data manipulation

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22

Convenience sample

Selects individuals from the population who are easy to reach.

Pro: cheap, efficient, and simple to implement

Con: produces unrepresentative data

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23

Voluntary response

People choose whether or not they want to be involved

Pro: an inexpensive way to conduct a study as data is very easy to gather

Con: the researcher has no control over the makeup of the sample. Untrustworthy

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24

Observational Study

observes individuals + measures variables of interest; doesn’t attempt to influence the responses

Pro: provide critical descriptive data and information on long-term efficacy and safety that clinical trials cannot provide, at generally much less expense

Con: lower standard of evidence than experimental studies, are more prone to bias and confounding, and cannot be used to demonstrate causality

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25

sampling bias

the design of a study shows bias if it’s likely to under/overestimate the value you want to know. It’s going to systematically fail due to a faulty design, resulting in the over/underestimations

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26

sampling variability

the idea that different random samples of the same size from the population produce different estimate. Reduce sampling variability by sample size, what we call error.

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27

confounding

occurs when 2 variables are associate in such a way that their effects on a response variable cannot be distinguished from each other

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28

experiment

deliberately imposes treatments on individuals to measure their response; show causation

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29

completely randomized design

an experiment done without blocking

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30

randomized block design

separate subjects into blocks and then randomly assign treatments within each blockb

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31

block

group of experimental units that are similar in some way that could be a cause of confounding. used to remove the effects of confounding

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32

matched pairs design

a common experimental design for comparing 2 treatments that uses blocks of size 2

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33

scope of inference

random sample (inference about population) + random assignment (inference about cause/effect)

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34

undercoverage

when some members of a population cannot or are less likely to be included in a sample

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35

nonresponse

when an individual is part of a survey but chooses not to respond or cannot be reached. this is different than voluntary response where people put themselves in the sample

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36

response bias

pattern of inaccurate results ( wording of a questions, interviewer, lying, etc.)

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37

explanatory variable

helps explain/predict response (on the x-axis)

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38

response variable

outcome being measured ( on the y-axis )

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