Hypothesis Testing

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# Hypothesis Testing - PowerPoint PPT Presentation

Hypothesis Testing. GTECH 201 Lecture 16. Overview of Today’s Topic. Formulation Evaluation Refining and Restating Statistical Tests. What is a Hypothesis?. Unproven or unsubstantiated statement You need to know the literature before you can formulate a hypothesis statement

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### Hypothesis Testing

GTECH 201

Lecture 16

Overview of Today’s Topic
• Formulation
• Evaluation
• Refining and Restating
• Statistical Tests
What is a Hypothesis?
• Unproven or unsubstantiated statement
• You need to know the literature before you can formulate a hypothesis statement
• Data collection should support hypothesis testing and evaluation
• If hypothesis is tested and found to be correct, then results can be refined (different scenarios can be tested)
• If partially correct, then hypothesis statement needs to be refined (reworded)
Hypothesis Testing
• Multi-step procedure that leads the researcher from the hypothesis statement to the decision regarding the hypothesis
• 6- step process
• State null and alternate hypotheses
• Select appropriate statistical test
• Select level of significance
• Delineate regions of rejection and nonrejection of hypotheses
• Calculate test statistic
• Make regarding null hypothesis
Step 1
• State null and alternate hypotheses
• Null hypothesis
• A hypothesis to be tested
• Usually represented as
• Alternative hypothesis
• A hypothesis considered as an alternate to the null hypothesis
• Usually represented as
Guidelines for Setting up H0, HA
• Hypothesis tests concerning one parameter
• Population mean, m
• A null hypothesis for a hypothesis test concerning a population mean should always specify a single value for that parameter
• (= ) sign must appear in the null hypothesis
• Therefore:
Guidelines, part 2
• Alternative hypothesis
• The choice of the alternative hypothesis depends on and should reveal the purpose of the hypothesis test
• Null hypothesis and alternative hypothesis are mutually exclusive
• Three choices are possible
Guidelines, part 3
• An alternate hypothesis with a sign is called a two-tailed test
• The population mean, is different from a specified value,
• When a < sign appears in the alternate hypothesis, the test is called a left-tailed test
• When a > sign appears in the alternate hypothesis, the test is called a right-tailed test
Setting up Hypotheses
• Asnack food company produces 454 gms bags of pretzels. Although the actual weights deviate slightly from the 454 gms, and vary from one bag to another, the quality control team insists that the mean net weight of bags be maintained at 454 gms. If the mean net weight of the bags is lower or higher, it is likely to cause problems.
• If you work for the quality control team and you want to decide whether the packaging machine is working properly, how would you set up a hypothesis test?

The packaging machine IS working properly

The packaging machine IS NOT working properly

Stating Hypotheses
Select Appropriate Test
• One sample difference of means t test
• Objective
• Compare a random sample mean to a population mean for difference
• Requirements and assumptions
• Random sample
• Normally distributed population
• Variable is measured at interval or ratio scale
• Hypotheses
• Test Statistic
Test Statistic
• sample mean
• population mean
• standard error of the mean
• population standard deviation
Level of Significance
•  = 0.10 (90%); 0.05 (95%); 0.01 (99.7%)
• Errors
• Type I error: Rejecting the null hypothesis when it is in fact true
• Type II error: Not rejecting the null hypothesis when it is in fact false
Identify Regions of Rejection
• Of null hypothesis
• Two-tailed
• Left tailed (directional)
• Right tailed (directional)
• Calculate test statistic
• Make decision regarding null or alternate hypothesis
To Work in Class
• We want to investigate demographic change in an area
• 3500 households (HH)
• You take a sample of 250 HH
• Sample mean = 2.68; sample variance =4.3
•  = 0.10 (90%)
• Now, we want to find out if the mean HH size in this one area is typical or representative of the national mean household size (2.61)
• Use the six step process to compare how closely the samples that you have taken compare with the national average HH size of 2.61
Limits of Hypothesis Testing
• Pre-selecting level of significance
• Lacks a theoretical basis
• Used for convenience
• Binary nature of null and alternative hypothesis
• P-value or Probability value
• Accepted approach
• The exact significance level associated with the calculated test statistic is determined
• We can define P-value as:
• The exact probability of getting a test statistic value of a given magnitude, IF the null hypothesis is true
• What is the probability of making a Type I error
• Type I error occurs when the null hypothesis is rejected using the hypothesis testing procedure, even though in reality the null hypothesis is true
Classical

State hypotheses

Decide on significance level

Select test

Delineate regions of rejection/nonrejection

Calculate the test statistic

P- Value

State hypotheses

Decide on significance level

Compute the value of the test statistic

Determine P-value

P reject null hypothesis; otherwise do not reject

Comparing Classical and P Value Approaches
Weak or none

Moderate

Strong

Very strong

Guidelines for Using P-Value

Evidence against H0

Example
• A random sample of 18 people with income below the poverty level reveals their daily intake of calcium
• mean 747.4 mg
• standard deviation 188 mg
• Use the P-value approach to determine whether the data provides sufficient evidence at the 5% significance level to conclude that the mean calcium intake of all Americans with income below the poverty level is less than the required daily allowance of 800 mg
Parametric and Nonparametric Tests
• Parametric tests
• Require knowledge about population parameters
• E.g., population is normally distributed
• Sample data measured on Interval/Ratio scale
• Non-parametric tests
• Requires no knowledge about population parameters
• Distribution-free
• Some non-parametric tests are designed to be applied for nominal, ordinal data ( ) – we will talk about these in the next lecture
Choices/Options
• Run only a parametric test
• Run only a non-parametric test
• Run both tests
• Goal
• State the problem
• Decide what inferential technique will be useful
• Identify formulae associated with the technique
• Interpret the results