• Study Resource
  • Explore
    • Arts & Humanities
    • Business
    • Engineering & Technology
    • Foreign Language
    • History
    • Math
    • Science
    • Social Science

    Top subcategories

    • Advanced Math
    • Algebra
    • Basic Math
    • Calculus
    • Geometry
    • Linear Algebra
    • Pre-Algebra
    • Pre-Calculus
    • Statistics And Probability
    • Trigonometry
    • other →

    Top subcategories

    • Astronomy
    • Astrophysics
    • Biology
    • Chemistry
    • Earth Science
    • Environmental Science
    • Health Science
    • Physics
    • other →

    Top subcategories

    • Anthropology
    • Law
    • Political Science
    • Psychology
    • Sociology
    • other →

    Top subcategories

    • Accounting
    • Economics
    • Finance
    • Management
    • other →

    Top subcategories

    • Aerospace Engineering
    • Bioengineering
    • Chemical Engineering
    • Civil Engineering
    • Computer Science
    • Electrical Engineering
    • Industrial Engineering
    • Mechanical Engineering
    • Web Design
    • other →

    Top subcategories

    • Architecture
    • Communications
    • English
    • Gender Studies
    • Music
    • Performing Arts
    • Philosophy
    • Religious Studies
    • Writing
    • other →

    Top subcategories

    • Ancient History
    • European History
    • US History
    • World History
    • other →

    Top subcategories

    • Croatian
    • Czech
    • Finnish
    • Greek
    • Hindi
    • Japanese
    • Korean
    • Persian
    • Swedish
    • Turkish
    • other →
 
Profile Documents Logout
Upload
ECON1003: Analysis of Economic Data - Ka
ECON1003: Analysis of Economic Data - Ka

N - PSI207
N - PSI207

Statistical Inference - Complementary Course of
Statistical Inference - Complementary Course of

PPT Lecture Notes
PPT Lecture Notes

Computing a confidence interval
Computing a confidence interval

How Does My TI
How Does My TI

Estimation - User Web Pages
Estimation - User Web Pages

SAS Essentials III: Statistics for Hamsters
SAS Essentials III: Statistics for Hamsters

... There are several reasons you may wish to have hamster-level statistics. First, you never had a statistics course, or it was so long ago that you were sitting between Fred Flintstone and Barney Rubble. You know that the mean is a bad measure for average income, but aren’t certain why. Perhaps, you h ...
Study Design I. Sample Size Consideration Tuan  V.  Nguyen
Study Design I. Sample Size Consideration Tuan V. Nguyen

Answers - UTSC - University of Toronto
Answers - UTSC - University of Toronto

... independence implies multiplication is ok for "and", so ans is 4/6 * 6/10 = 0.40. (a) 0.53 (b) * 0.40 (c) 0.27 (d) 0.20 (e) 0.13 24. A simple random sample of 50 measurements is taken from a slightly skewed population whose standard deviation is known to be 10. We are testing a null hypothesis that ...
Chapter 1 Statistical Distributions
Chapter 1 Statistical Distributions

... between rats within each treatment is much larger. In this case we might feel that the observed difference is just a chance event. This example should make it clear that it is the size of the observed effect in relation to the variation within each experimental group that is important in making a st ...
File - Shelbi`s e
File - Shelbi`s e

PPT 09
PPT 09

Analyzing Quantitative Data - The Learning Store
Analyzing Quantitative Data - The Learning Store

... This is the time to work with your data. Look at the findings from different angles. Check for patterns. Begin to frame your data into charts, tables, lists and graphs to view the findings more clearly and from different perspectives. A good process is to summarize all your data into tables and char ...
Math 116 – Activity 1 – Part 1 - Montgomery College Student Web
Math 116 – Activity 1 – Part 1 - Montgomery College Student Web

... To roughly estimate the minimum and maximum “usual” sample values, use: Minimum “usual” value ~ mean – 2 * standard deviation Maximum “usual” value ~ mean + 2 * standard deviation ...
Applied Data Analysis - University of Rochester
Applied Data Analysis - University of Rochester

... The margin of error is twice the standard error. (Why twice?) The poll number are therefore: expected value ± (2xS.E.) ...
Statistical Sampling Overview and Principles
Statistical Sampling Overview and Principles

SUFFICIENT STATISTICS 1. Introduction Let X = (X 1,...,Xn) be a
SUFFICIENT STATISTICS 1. Introduction Let X = (X 1,...,Xn) be a

... is unknown. We are interested using X to estimate θ. In the simple case where Xi ∼ Bern(p), we found that the sample mean was an efficient estimator for p. Thus, if we observe a finite sequence of coin flips, in order to have an efficient estimate of the probability, p, that heads occurs in a single ...
Use the information given below to answer questions 1
Use the information given below to answer questions 1

... QBM117 Multiple Choice Test – Spring 2003 ...
MDST242 C2 - The Open University
MDST242 C2 - The Open University

Estimation in Sampling!?
Estimation in Sampling!?

MATH20802: STATISTICAL METHODS EXAMPLES
MATH20802: STATISTICAL METHODS EXAMPLES

Basic Descriptive Statistics
Basic Descriptive Statistics

Linear Regression
Linear Regression

2.23 One Quantitative Variable
2.23 One Quantitative Variable

< 1 ... 53 54 55 56 57 58 59 60 61 ... 285 >

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.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report