{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Chapter11_STAT1100.ppt", filename="Chapter11_STAT1100.ppt", filename="Chapter11_

# Chapter11_STAT1100.ppt", filename="Chapter11_STAT1100.ppt", filename="Chapter11_

This preview shows pages 1–10. Sign up to view the full content.

Introduction to Hypothesis Testing Statistics for Management and Economics Chapter 11

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Objectives Concepts of Hypothesis Testing Testing the Population Mean when the Population Standard deviation is known Calculating the Probability of Type II Error
Introduction to Hypothesis Testing In addition to estimation, hypothesis testing is a procedure for making inferences about a population. Parameter Population Sample Statistic Inference

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Introduction to Hypothesis Testing Hypothesis testing typically begins with some theory, claim, or assertion about a particular parameter of a population. When we have a guess, or an idea, about something that we expect to happen, this is called a hypothesis . Hypothesis testing allows us to determine whether enough statistical evidence exists to conclude that a belief (i.e. hypothesis) about a parameter is supported by the data.
Concepts of Hypothesis Testing There are two hypotheses. One is called the null hypothesis and the other the alternative or research hypothesis . The usual notation is: H 0 : — the ‘null’ hypothesis H 1 : — the ‘alternative’ or ‘research’ hypothesis pronounced H “nought” Sometimes  H a

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
The Null Hypothesis H 0 The null hypothesis (H 0 ) is always a hypothesis of status quo or no difference. This is the hypothesis that is tested. Always refers to a specified value of the population parameter (such as μ ), not a sample statistic (such as x-bar). Always contains an equal sign regarding the specified value of the population parameter. H 0 : μ = 368 grams
The Alternative Hypothesis (H 1 ) The alternative hypothesis (H 1 ) is the opposite of the null hypothesis. Represents the conclusion supported if the null hypothesis is rejected. Always refers to a specified value of the population parameter (such as μ ), not a sample statistic (such as x-bar). Can be directional (<, >) or not ( ), but never contains an equal sign regarding the specific value of the population parameter. Usually represents what you expect to happen. H 1 : μ 368 grams

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Hypothesis testing is designed so that the rejection of the null hypothesis is based on evidence from the sample. Failure to reject the null hypothesis is not proof that it is true. The testing procedure begins with the assumption that the null hypothesis is true . Thus, until we have further statistical evidence, we will assume : H 0 : μ = 368 (assumed to be TRUE) Concepts of Hypothesis Testing
The goal of the process is to determine whether there is enough evidence to infer that the alternative hypothesis is true. That is, is there sufficient statistical information to determine if this statement: H 1 : ≠ 350, is true? We either reject or fail to reject the null hypothesis – one can never prove that the null hypothesis is correct because the decision is made based on the sample, not the entire population.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}