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9.1 Introduction to Hypothesis Testing
9.1 Introduction to Hypothesis Testing

Chapter 14
Chapter 14

Null and Alternative Hypotheses
Null and Alternative Hypotheses

... 1. Evaluate the null hypothesis, typically denoted with H0 . The null is not rejected unless the hypothesis test shows otherwise. The null statement must always contain some form of equality (=, ≤ or ≥) 2. Always write the alternative hypothesis, typically denoted with Ha or H1 , using less than, gr ...
Math 123- Statistics Name_______________________________
Math 123- Statistics Name_______________________________

... A Type I error is the worst type of error in this case because a person was innocent and they were shown to be guilty. A Type II error is slightly less offensive in this case because a guilty person who is shown to be not guilty at this time always has the chance of being caught again in the future. ...
Chapter 4 Hypothesis Testing1
Chapter 4 Hypothesis Testing1

TestOfHypothesis - Asia University, Taiwan
TestOfHypothesis - Asia University, Taiwan

... • Considered the following set of measurements for a given population: 55.20, 18.06, 28.16, 44.14, 61.61, 4.88, 180.29, 399.11, 97.47, 56.89, 271.95, 365.29, 807.80, 9.98, 82.73. The population mean is 165.570. • Now, considered two samples from this population. • These two different samples could h ...
Hypothesis testing 101 What is a hypothesis? A hypothesis an idea
Hypothesis testing 101 What is a hypothesis? A hypothesis an idea

... What does a hypothesis test do? A hypothesis test cannot prove or disprove the null hypothesis. We can only determine that based on the data we have if there is enough evidence to reject the null hypothesis or not. Based on our decision to reject or not we interpret what that means in terms of the s ...
Chapter 2 - UniMAP Portal
Chapter 2 - UniMAP Portal

H 0
H 0

Lecture_20_ch11_222_w05_s123
Lecture_20_ch11_222_w05_s123

Hypothesis Testing
Hypothesis Testing

Hypothesis Testing
Hypothesis Testing

you will have to put the appropriate sign on the critical value, +, –, or
you will have to put the appropriate sign on the critical value, +, –, or

... A Hypothesis is a statement (claim) regarding a characteristic of one or more populations. Hypothesis Testing is a procedure, based on sample evidence and probability, used to test statements (claims) regarding a characteristic of one or more populations. Because we use sample data to test hypothese ...
Chapter 9
Chapter 9

6-D - rlhawkmath
6-D - rlhawkmath

... the amount of wine put in a bottle was 0.23 ounces. With new equipment, the quality control manager believes the standard deviation has decreased. ...
Quantitative Research methods for the Social Science, 7.5 hp
Quantitative Research methods for the Social Science, 7.5 hp

- Northumbria Research Link
- Northumbria Research Link

... Generally speaking, there are two interpretations of probability in statistics. The first is subjective and the second objective. Subjective probability is the most intuitive and describes a personal degree of belief that an event will occur. It also forms the basis of the Bayesian method of inferen ...
Chapter 7
Chapter 7

Example Is the New Drug Better?
Example Is the New Drug Better?

... Excerpts from the article “Stress can cause sneezes” (The New York Times, January 21, 1997) are shown at the right. Studies suggest that stress doubles a person’s risk of getting a cold. Acute stress, lasting maybe only a few minutes, can lead to colds. One mystery that is still prevalent in cold re ...
Design of Engineering Experiments Part 2
Design of Engineering Experiments Part 2

... Importance of the t-Test • Provides an objective framework for simple comparative experiments • Could be used to test all relevant hypotheses in a two-level factorial design, because all of these hypotheses involve the mean response at one “side” of the cube versus the mean response at the opposite ...
Hypothesis Test Summary
Hypothesis Test Summary

Hypothesis Testing
Hypothesis Testing

... cases, we say that the hypothesis has been rejected at the α level of significance. ...
Chapter 7 Hypothesis Testing
Chapter 7 Hypothesis Testing

... Basic Idea for Hypothesis Tests State the hypothesis. Gather sample data (“evidence”). Make a decision about your hypothesis, based on sample data. ...
Topic 9 One-Sample Hypothesis Tests INTRODUCTION TO ECONOMIC STATISTICS
Topic 9 One-Sample Hypothesis Tests INTRODUCTION TO ECONOMIC STATISTICS

Using the decision tree - Illinois State University
Using the decision tree - Illinois State University

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Statistical hypothesis testing

A statistical hypothesis is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. A statistical hypothesis test is a method of statistical inference. Commonly, two statistical data sets are compared, or a data set obtained by sampling is compared against a synthetic data set from an idealized model. An hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. The comparison is deemed statistically significant if the relationship between the data sets would be an unlikely realization of the null hypothesis according to a threshold probability—the significance level. Hypothesis tests are used in determining what outcomes of a study would lead to a rejection of the null hypothesis for a pre-specified level of significance. The process of distinguishing between the null hypothesis and the alternative hypothesis is aided by identifying two conceptual types of errors (type 1 & type 2), and by specifying parametric limits on e.g. how much type 1 error will be permitted.An alternative framework for statistical hypothesis testing is to specify a set of statistical models, one for each candidate hypothesis, and then use model selection techniques to choose the most appropriate model. The most common selection techniques are based on either Akaike information criterion or Bayes factor.Statistical hypothesis testing is sometimes called confirmatory data analysis. It can be contrasted with exploratory data analysis, which may not have pre-specified hypotheses.
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