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22 Cards in this Set
- Front
- Back
Statistically Significant
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if the observed effect is larger than the anticipated results and larger than chance variation/randomization alone
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Experiments & Samples use randomization to get unbiased data but they do so in different ways and for different purposes
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Sample Surveys try to estimate population parameters
Experiments try to assess the effects of treatments |
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Control Treatments
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A treatment that doesn't contain anything used as a benchmark against a group that does receive an actual treatment
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Blinding
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prevent the subjects from know which treatment they are receiving, researcher should also be blinded too
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There are 2 main classes of individuals who can affect the outcome of the experiment
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1. Those who could influence the results (subjects, treatment admins, or techs)
2. Those who evaluate the results (judges, treating physicians etc.) When all the individuals in either one of these classes are blinded, an experiment is said to be <b>Single Blinded</b> When everyone in BOTH classes is blinded, we call the experiment <b>double-blinded<b/> |
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A "fake" treatment that looks just like the treatments being tested is called a
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Placebo
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The fact that subjects treated witha placebo somtimes improve highilights what?
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Effective Blinding
Importance of comparing treatments with a control |
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The best experiments are usually
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Randomized
Comparative Double blind Placebo Controlled |
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When groups of experimental units are similiar, it's often a good idea to gather them together into blocks.
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True
By blocking we isolate the variability attributable to the differences between the blocks, so that we can see the differences caused by the treatments more clearly |
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When randomization occurs only once the blocks have been established we call this a _______
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Randomized block design
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Matching Subjects is pairing subjects in a way that they are similiar not under study
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true
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Blocking --- Experiment
Stratifying --- Sampling |
Yes
Blocking & Stratifying are equivalent in meaning but not interchangeable |
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Confounded
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When the levels of one factor are associate with the levels of another factor (combining explanatory variables, x values)
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Confounding & Lurking Variables are alike in that they interfere with our ability to interpret our analyses simply.
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True
Lurking variable: is usually though of as a variable associated with both Y and X that makes it appear that X may be causing Y (creates an association between two other variables that tempts us to think that one may cause the other.) Confounding can arise in experiments when some other variable associated with a factor has an effect on the response variable. A confounding variable is associated in a non-causal way with a factor and affects the response. Because of the confounding, we find that we can't tell whether any effect we see was caused by our factor or by the confounding variable--or by both working together. |
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Three Things you should always do first with the data:
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Make a picture
Make a picture Make a picture |
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Frequency Table
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table that records the total counts and the category names
A relative Frequency table displays the percentages, rather than the counts of the values in each category. Both types of tables show how the cases are distributed across the categories. In this way, they describe the distribution of a categorical variable because they name the possible categories and tell how frequently each occurs |
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THe best data displays observe a fundamental principle of graphing data called the ____
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Area principle
the A. principle says that the area occupied by a part of the graph should correspond to the magnitude of the value it represents |
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Bar Charts
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displays the distribution of a categorical variable, showing the counts for each category next to each other for easy comparison.
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Contingency Table
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table showing when one variables distribution is contingent or depends on the value of another
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Histograms
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usually we slice up all the possible values into equal width bins. We then count the number of cases that fall into each bin. The bins, together with these counts, give the distribution of the quantative varialbe and provided the building blocks for the hitogram. By representing the counts as bars and plotting them against the bin values, the histogram displays the disti at a glance.
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Bar chart & histograms are not the same display
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true
you CAN'T display categorical data in a histogram or quantitative data in a bar chart. |
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When looking at a Quantitative display (histogram, stemandleaf plot) and you look at its distribution what should you look for?
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Shape-Does the histogram have a single, central hump or several separated humps? These humps are called <b>modes</b>. A histogram with one peak, such as the earthquake magnitudes, is dubbed <b>unimodal</b> histograms with 2 peaks are <b>bimodal</b> and those with three or more are called <b>multimodal<b/>. no mode is called UNIFORM.
Center Spread |