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Research Methodology and Statistics Module
Research Methodology and Statistics Module

Globally Robust Inference
Globally Robust Inference

week 1 - SPIA UGA
week 1 - SPIA UGA

Basic Statistics on a TI-83/84 Entering Data in Lists There are six
Basic Statistics on a TI-83/84 Entering Data in Lists There are six

Hypothesis Testing
Hypothesis Testing

Unit 21 Student`s t Distribution in Hypotheses Testing
Unit 21 Student`s t Distribution in Hypotheses Testing

( ! ) Notice: Undefined index
( ! ) Notice: Undefined index

Describing Data - Descriptive Statistics
Describing Data - Descriptive Statistics

class notes - rivier.instructure.com.
class notes - rivier.instructure.com.

... ANOVA for Repeated-Measures Design: This is where the same sample is used multiple times, or that the same study group of individuals participates in all of the different treatment conditions. * The general purpose of a repeated-measures ANOVA is to determine whether the differences that are found b ...
One-Sample T-Test Chapter 205 Introduction
One-Sample T-Test Chapter 205 Introduction

Mann-Whitney U test - E
Mann-Whitney U test - E

One-Sample T-Test
One-Sample T-Test

Notes 2 - Wharton Statistics
Notes 2 - Wharton Statistics

... A 95% confidence interval for a population parameter should have the following property: If we take samples from our population over and over again and construct a confidence interval using our procedure for each of the samples, 95% of the resulting intervals should include the true value of the po ...
Hypothesis Testing - one sample.
Hypothesis Testing - one sample.

Chapter 7: Two–Sample Inference
Chapter 7: Two–Sample Inference

Chapter 2: Descriptive Statistics
Chapter 2: Descriptive Statistics

Homework 9 Solutions 1. Note that we have the following summary
Homework 9 Solutions 1. Note that we have the following summary

... µdiff > 0. Population mean on attempt 2 is greater population mean on attempt 1 Compute the one-sample t-statistic as ...
A Note on Standard Deviation and RMS
A Note on Standard Deviation and RMS

... population quantities. When we substitute measured values into these rules for estimators, we obtain a single number called an estimate. We are interested in three distinct definitions for the population quantities. For example, the mean can have three different connotations. The first is the theore ...
Multiple Sample Comparison SnapStat
Multiple Sample Comparison SnapStat

TEQIP_Part 3
TEQIP_Part 3

PROBABILITY TOPICS: HOMEWORK
PROBABILITY TOPICS: HOMEWORK

... l. Are there any outliers in the data? Use an appropriate numerical test involving the IQR to identify outliers, if any, and clearly state your conclusion. m. Are any data values further away than 2 standard deviations from the mean? Clearly state your conclusion and show numerical work to justify y ...
Chapter 4: Random Variables and Probability Distributions
Chapter 4: Random Variables and Probability Distributions

Module Handbook - Banks and Markets
Module Handbook - Banks and Markets

... 1. If a sample of 100 items is drawn from a population and the mean is found to be 200g with a standard deviation of 5g, find: (a) a 95% confidence interval estimate for the population mean. (b) a 99% confidence interval estimate for the population mean. (c) if a sampling error of only ± 0.5g is all ...
PROBABILITY TOPICS: HOMEWORK
PROBABILITY TOPICS: HOMEWORK

Chapter 7
Chapter 7

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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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