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Statistical testing vs. interval estimation
Statistical testing vs. interval estimation

ADVANCED PLACEMENT (AP) STATISTICS Grades 10, 11, 12
ADVANCED PLACEMENT (AP) STATISTICS Grades 10, 11, 12

True/False Questions - Academic Information System (KFUPM AISYS)
True/False Questions - Academic Information System (KFUPM AISYS)

slides - Courses
slides - Courses

Using Excel to do Statistics
Using Excel to do Statistics

IT-Enabled Process Improvement
IT-Enabled Process Improvement

... To make legitimate conclusions about the population, two characteristics must be present within the sample: Representative sampling: While this might be feasible for manufacturing processes, it is much more problematic for software processes. Statistical assumptions: The validity of a statistical ...
The Null Hypothesis
The Null Hypothesis

... A type II error, also known as an error of the second kind, occurs when the null hypothesis is false, but it is erroneously accepted as true. It is failing to assert what is present, a miss. A type II error may be compared with a so-called false negative (where an actual 'hit' was disregarded by the ...
1. Use the confidence level and sample data to find a confidence
1. Use the confidence level and sample data to find a confidence

... Standard Error, SE = s/√n = 6.8/√105 = 0.6636 Degrees of freedom = 104 t- score = 2.6239 Width of the confidence interval = t * SE = 2.6239 * 0.6636 = 1.7413 Lower Limit of the confidence interval = x-bar - width = 70.5 - 1.7413 = 68.7587 Upper Limit of the confidence interval = x-bar + width = 70.5 ...
SPSS 101 - University of San Diego Home Pages
SPSS 101 - University of San Diego Home Pages

Document
Document

... • The goal of a test of significance is to test the evidence provided by data about some claim, called a null hypothesis, concerning a parameter of the population – an outcome that would rarely happen if a claim were true is good evidence that the claim is not true • this is based on the idea of a c ...
Document
Document

Ch. 15 Review #1
Ch. 15 Review #1

Lecture 11--1
Lecture 11--1

Use the computational formula for s X
Use the computational formula for s X

Exam Review Sheet
Exam Review Sheet

... a. Define appropriate statistical variables, and use them to state the null and alternative hypotheses that would be used to decide if there was convincing evidence against the hypothesized distribution of purchases across the three brands. b. Suppose that each individual in a random sample of 200 p ...
Ankenman`s Statistics Lecture Slides
Ankenman`s Statistics Lecture Slides

Ex St 801 Statistical Methods Inference about a Single Population
Ex St 801 Statistical Methods Inference about a Single Population

Test 1 - La Sierra University
Test 1 - La Sierra University

... 10. (Similar to RS#13) Use the formulas x   xf, and x2   x2f where on the right hand side we use the class midpoints and frequencies. Then x 4.523 + 14.557 + 24.520 = 1420 x2  4.5223 + 14.5257 + 24.5220 = 24,455 and SSx  24,455 – (1420)2/100 = 4291 Therefore, the mean is approxima ...
Statistical Methods A Brief Review
Statistical Methods A Brief Review

AP Statistics - UH Mathematics Contest
AP Statistics - UH Mathematics Contest

Problem 2.21 last part of the table has the years mixed up. You can
Problem 2.21 last part of the table has the years mixed up. You can

... environmental issues? Use a 5% level of significance. B) Based on your decision, which type of error could the sample data have led you to make? 12. A soft-drink manufacturer claims that its 12-ounce cans do not contain, on average, more than 30 calories. A random sample of 64 cans of this soft drin ...
The worksheet is available as a Word document
The worksheet is available as a Word document

Results PowerPoint
Results PowerPoint

Inference as Decision
Inference as Decision

Govt_3990_Course_Assessment 1. This is a distribution of test
Govt_3990_Course_Assessment 1. This is a distribution of test

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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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