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descriptive stats
descriptive stats

Drawing a Sample
Drawing a Sample

Exam 3
Exam 3

... The health of the bear population in Yellowstone National Park is monitored by periodic measurements taken from an anesthetized bears. A sample of 54 bears has a mean weight of 182.9lb. Let  be population mean of all such bear weights and  is 121.8lb and assume that data are normally distributed. ...
Statistics I want to see in your presentation
Statistics I want to see in your presentation

... Note that the raw data includes the units measured as well as a breakdown of each test subject; note also that test subject DD died not as a result of the study but from other causes. It is important to include the cause of death because if a second test subject was to drop dead from an anvil attack ...
LECTURE 14 (Week 5)
LECTURE 14 (Week 5)

... Red wine, in moderation Drinking red wine in moderation may protect against heart attacks. The polyphenols it contains act on blood cholesterol and thus are a likely cause. To test the hypothesis that moderate red wine consumption increases the average blood level of polyphenols, a group of nine ra ...
Note on sampling - Nuffield Foundation
Note on sampling - Nuffield Foundation

... to: understand the principles of sampling as applied to scientific data. This note is to clarify what we mean by the statement. Researchers might be interested in questions such as whether a diet rich in fruit and vegetables can reduce cancer incidence, or whether higher carbon dioxide concentration ...
AMS 572 Lecture Notes #6 September 24, 2013
AMS 572 Lecture Notes #6 September 24, 2013

Dissertation preparation 4
Dissertation preparation 4

... towards the pointed end with respect to the mode and median In such cases, the different averages can give very different impressions of the data. The mean, in particular, can be very misleading if it is reported as reflecting a “typical” score. It is often informative to report more than one ...
lambda
lambda

Case Study
Case Study

and T-tests
and T-tests

... • Minimize error variance – Reduce error through controlling experimental conditions – Reduce error by increasing reliability of measures ...
Section 2
Section 2

1 Statistical Tests of Hypotheses
1 Statistical Tests of Hypotheses

Hypothesis Testing Hypothesis Testing Example Example
Hypothesis Testing Hypothesis Testing Example Example

More Statistics
More Statistics

2030Lecture4
2030Lecture4

P-value - Department of Statistics and Probability
P-value - Department of Statistics and Probability

... COME FROM A DISTRIBUTION THAT IS UNIMODAL AND SYMMETRIC. REMARK: ...
Bootstrap and cross
Bootstrap and cross

PPT - StatsTools
PPT - StatsTools

... > We already discussed that statistically significant does not always mean practically important. • Be especially skeptical if they have very large sample sizes (without any of the information presented in this chapter). ...
Title of slide - Royal Holloway, University of London
Title of slide - Royal Holloway, University of London

stat_14
stat_14

... (Myth:  = 1 is “conservative”) Can separate out different systematic for same measurement ...
here - BCIT Commons
here - BCIT Commons

... most authors simply say something along the lines "as long as s12 and s22 are not too different, you're probably all right in assuming 12 = 22." Since they don't specify what they mean by "too different", this advice is rather useless. some authors (for example, Jarrell, p 468) actually give a rul ...
Chapter 9
Chapter 9

Testing the Population Variance
Testing the Population Variance

... During annual checkups physician routinely send their patients to medical laboratories to have various tests performed. One such test determines the cholesterol level in patients’ blood. However, not all tests are conducted in the same way. To acquire more information, a man was sent to 10 laborator ...
Hypothesis Testing Quiz - Chapter 10
Hypothesis Testing Quiz - Chapter 10

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Misuse of statistics

Statistics are supposed to make something easier to understand but when used in a misleading fashion can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives.Misuses can be easy to fall into. Professional scientists, even mathematicians and professional statisticians, can be fooled by even some simple methods, even if they are careful to check everything. Scientists have been known to fool themselves with statistics due to lack of knowledge of probability theory and lack of standardization of their tests.
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