chi-square test
A statistical significance test based on frequency of occurrence; it is applicable both to qualitative attributes and quantitative variables.
Among its many uses, the most common are tests of hypothesized probabilities or probability distributions (goodness of fit), statistical dependence or independence (association), and common population (homogeneity). The formula for chi square (χ2) depends upon intended use, but is often expressible as a sum of terms of the type (f - h)2/h where f is an observed frequency and h its hypothetical value.
chi-square = Sum ( (f - h)2/h )
f is an observed frequency and
h its hypothetical value.
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More detailed explanation:
Chi-Square Calculator: Online Statistical Table
The chi-square distribution calculator makes it easy to compute cumulative probabilities, based on the chi-square statistic. For help, read the Frequently-Asked Questions or review the Sample Problems.To learn more about the chi-square distribution, read Stat Trek's tutorial on the chi-square distribution.
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Frequently-Asked Questions
Instructions: To find the answer to a frequently-asked question, simply click on the question. If you don't see the answer you need, read Stat Trek's tutorial on the chi-square distribution or visit the Statistics Glossary.
- What are degrees of freedom?
- What is a chi-square critical value?
- What is a cumulative probability?
- What is a chi-square statistic?
- What is a probability?
Degrees of freedom can be described as the number of scores that
are free to vary. For example, suppose you tossed three dice. The total score
adds up to 12. If you rolled a 3 on the first die and a 5 on the second, then
you know that the third die must be a 4 (otherwise, the total would not add up
to 12). In this example, 2 die are free to vary while the third is not.
Therefore, there are 2 degrees of freedom.
In many situations, the degrees of freedom are equal to the
number of observations minus one. Thus, if the sample size were 20, there would
be 20 observations; the the degrees of freedom would be 20 minus 1 or 19.
The chi-square critical value can be any number between
zero and plus infinity.
The chi-square calculator computes the probability that a chi-square statistic
falls between 0 and the critical value.
Suppose you randomly select a sample of 10
observations from a large population.
In this example, the degrees of freedom (DF) would be 9,
since DF = n - 1 = 10 - 1 = 9.
Suppose you wanted to find the probability that a chi-square statistic falls between
0 and 13. In the chi-square calculator, you would enter 9 for degrees of freedom
and 13 for the critical value. Then, after you click the Calculate button, the
calculator would show the cumulative probability to be 0.84.
A cumulative probability is a sum of probabilities.
The chi-square calculator computes a cumulative probability. Specifically,
it computes the probability that a
chi-square statistic falls between 0 and some critical value (CV).
With respect to notation,
the cumulative probability that a chi-square statistic
falls between 0 and CV is indicated by P(Χ2 < CV).
A chi-square statistic is a
statistic
whose values are given by
Χ2 = [ ( n - 1 )
* s2 ] / σ2
where σ is the standard deviation of
the population, s is the standard deviation of the sample, and n is the sample
size. The distribution of the chi-square statistic has n - 1 degrees of
freedom. (For more on the chi-square statistic, see the
tutorial on the chi-square distribution.)
A probability is a number expressing the chances that a specific
event will occur. This number can take on any value from 0 to 1. A probability
of 0 means that there is zero chance that the event will occur; a probability
of 1 means that the event is certain to occur. Numbers between 0 and 1 quantify
the uncertainty associated with the event. For example, the probability of a
coin flip resulting in Heads (rather than Tails) would be 0.50. Fifty percent
of the time, the coin flip would result in Heads; and fifty percent of the
time, it would result in Tails.
Chi-Square Distribution: Sample Problems
-
The Acme Widget Company claims that their widgets last 5
years, with a standard deviation of 1 year. Assume that their claims are true.
If you test a random sample of 9 Acme widgets, what is the probability that the standard deviation in your sample will be less than 0.95 years?
Solution:
We know the following:
- The population standard deviation is equal to 1.
- The sample standard deviation is equal to 0.95.
- The sample size is equal to 9.
- The degrees of freedom is equal to 8 (because sample size minus one = 9 - 1 = 8).
Given these data, we compute the chi-square statistic:
Χ2 = [ ( n - 1 ) * s2 ] / σ2
Χ2 = [ ( 9 - 1 ) * (0.95)2 ] / (1.0)2 = 7.22
where σ is the standard deviation of the population, s is the standard deviation of the sample, and n is the sample size.
Now, using the Chi-Square Distribution Calculator, we can determine the cumulative probability for the chi-square statistic. We enter the degrees of freedom (8) and the chi-square statistic (7.22) into the calculator, and hit the Calculate button. The calculator reports that the cumulative probability is 0.49. Therefore, there is a 49% chance that the sample standard deviation will be no more than 0.95. -
Find the chi-square critical value, if the cumulative
probability is 0.75 and the sample size is 25.
Solution:
We know the following:
- The cumulative probability is 0.75.
- The sample size is 25.
- The degrees of freedom is equal to 24 (because sample size minus one = 25 - 1 = 24).
Given these data, we compute the chi-square statistic, using the Chi-Square Distribution Calculator. We enter the degrees of freedom (24) and the cumulative probability (0.75) into the calculator, and hit the Calculate button. The calculator reports that the chi-square critical value is 28.2.
This means that if you select a random sample of 25 observations, there is a 75% chance that the chi-square statistic from that sample will be less than or equal to 28.2.
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