Hypothesis Tecsting

Topics: Statistical tests, Statistics, Non-parametric statistics Pages: 116 (6986 words) Published: March 2, 2014
Testing of Hypothesis:
(Non-parametric Tests)
Chapter-11: Testing of Hypothesis - (Non-parametric Tests)

11.1. Chi - square ( χ )Test / Distribution

11.1.1. Meaning of Chi - square ( χ )Test

11.1.2. Characteristics of Chi - square ( χ )Test

11.2. Types of Chi - square ( χ )Test / Distribution

11.2.1. Chi - square ( χ )Test for Population Variance

11.2.2. Chi - square ( χ )Test for Goodness-of-Fit

11.2.3. Chi - square ( χ )Test or Independence
11.3. Analysis of Variance (ANOVA)
11.3.1. Meaning of ANOVA
11.3.2. ANOVA Approach
11.4. ANOVA Technique
11.4.1. One-way ANOVA
11.4.2. Two-way ANOVA
11.4.3. ANOVA in Latin-square Design
11.5. Other Nonparametric Techniques

Key Terms:
11.1. CHI-SQUARE (


11.1.1. Meaning of Chi - square ( χ )Test

A chi-square test (also chi squared test or χ test) is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-square distribution when the null hypothesis is true, or any in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chi-square distribution as closely as desired by making the sample size large enough. The Chi-Square (

) test is the most popular non-parametric
test/methods, to test the hypothesis. The symbol
is the Greek letter “chi”. Like other hypothesis
testing procedures, the calculated value of
-test statistics is compared with its critical value to
know whether the null hypothesis is true.

Generally the chi-squared statistic summarizes the discrepancies between the expected number of times each outcome occurs (assuming that the model is true) and the observed number of times each outcome occurs, by summing the squares of the discrepancies, normalized by the expected numbers, over all the categories.

Data used in a chi-square analysis has to satisfy the following conditions 1.

Randomly drawn from the population,
Reported in raw counts of frequency,
Measured variables must be independent,
Observed frequencies cannot be too small, and
Values of independent and dependent variables must be mutually exclusive.

11.1.2. Characteristics of the Chi-Square Test:
1. It is not symmetric.
2. The shape of the chi-square distribution depends upon the degrees of freedom, just like Student’s t-distribution.
3. As the number of degrees of freedom increases, the chi-square distribution becomes more symmetric as is illustrated in Figure 1.
4. The values are non-negative. That is, the values of are greater than or equal to 0. 11.2. TYPES OF CHI-SQUARE TEST / DISTRIBUTION:
There are three types of chi-square test.

Chi square test for population variance which is used to test a hypothesis on a specific value of the population variance.
2. The Chi-square test for goodness of fit which compares the expected and observed values to determine how well an experimenter's predictions fit the data. 3. The Chi-square test for independence which compares two sets of categories to determine whether the two groups are distributed differently among the categories. 1.


-test for Population Variance:

Chi square test for population variance is used to test a hypothesis on a specific value of the population variance. Statistically speaking, we test the null hypothesis H0: σ = σ0 against the research hypothesis H1: σ # σ0 where σ is the population mean and σ0 is a specific value of the population variance that we would like to test for acceptance.

If a sample of size n is taken from a population having a normal distribution, then there is a wellknown result which allows a test to be made of whether the variance of the population has a predetermined value. The test statistic t in this instance could be set to be the sum of squares about the sample mean, divided by the nominal value for the variance then t has a...
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