Stats Exam 1 Assignment #3

Front 1

Statistically Significant

Back 1

if the observed effect is larger than the anticipated results and larger than chance variation/randomization alone

Front 2

Experiments & Samples use randomization to get unbiased data but they do so in different ways and for different purposes

Back 2

Sample Surveys try to estimate population parameters

Experiments try to assess the effects of treatments

Front 3

Control Treatments

Back 3

A treatment that doesn't contain anything used as a benchmark against a group that does receive an actual treatment

Front 4

Blinding

Back 4

prevent the subjects from know which treatment they are receiving, researcher should also be blinded too

Front 5

There are 2 main classes of individuals who can affect the outcome of the experiment

Back 5

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/>

Front 6

A "fake" treatment that looks just like the treatments being tested is called a

Back 6

Placebo

Front 7

The fact that subjects treated witha placebo somtimes improve highilights what?

Back 7

Effective Blinding

Importance of comparing treatments with a control

Front 8

The best experiments are usually

Back 8

Randomized

Comparative

Double blind

Placebo Controlled

Front 9

When groups of experimental units are similiar, it's often a good idea to gather them together into blocks.

Back 9

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

Front 10

When randomization occurs only once the blocks have been established we call this a _______

Back 10

Randomized block design

Front 11

Matching Subjects is pairing subjects in a way that they are similiar not under study

Back 11

true

Front 12

Blocking --- Experiment

Stratifying --- Sampling

Back 12

Yes

Blocking & Stratifying are equivalent in meaning but not interchangeable

Front 13

Confounded

Back 13

When the levels of one factor are associate with the levels of another factor (combining explanatory variables, x values)

Front 14

Confounding & Lurking Variables are alike in that they interfere with our ability to interpret our analyses simply.

Back 14

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.

Front 15

Three Things you should always do first with the data:

Back 15

Make a picture

Make a picture

Make a picture

Front 16

Frequency Table

Back 16

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

Front 17

THe best data displays observe a fundamental principle of graphing data called the ____

Back 17

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

Front 18

Bar Charts

Back 18

displays the distribution of a categorical variable, showing the counts for each category next to each other for easy comparison.

Front 19

Contingency Table

Back 19

table showing when one variables distribution is contingent or depends on the value of another

Front 20

Histograms

Back 20

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.

Front 21

Bar chart & histograms are not the same display

Back 21

true

you CAN'T display categorical data in a histogram or quantitative data in a bar chart.

Front 22

When looking at a Quantitative display (histogram, stemandleaf plot) and you look at its distribution what should you look for?

Back 22

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.

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