Test of two distributions/functions

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In summary, the question is whether f1(x,y) and f2(x,y) are significantly different and if there is a way to test this. However, statistical significance is only meaningful when dealing with phenomena involving probability and without knowing the specific process, it is impossible to determine an appropriate test.
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phonic
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Dear All,

I have two functions that are defined on a table, i.e. [itex]f1(x,y), f2(x,y)[/itex], where x and y are bin indices, and [itex] 1\leq f1(x,y) \leq 1 [/itex], [itex] 1\leq f2(x,y) \leq 1 [/itex].

I would like to perform some test to show whether [itex]f1(x,y)[/itex] and [itex]f2(x,y)[/itex] are significantly different. Is there some way to do this? I thought of using chi square test for [itex]f1(x,y)+n[/itex] and [itex]f2(x,y)+n[/itex], where n>1 is a constant added to make [itex]f1(x,y)[/itex] and [itex]f2(x,y)[/itex] interpretable as frequencies.

Thanks a lot!
 
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  • #2
phonic said:
I thought of using chi square test for [itex]f1(x,y)+n[/itex] and [itex]f2(x,y)+n[/itex], where n>1 is a constant added to make [itex]f1(x,y)[/itex] and [itex]f2(x,y)[/itex] interpretable as frequencies.

Statistical signficance is not a meaningful concept unless you are dealing with a phenomena that involves probability. To do the usual type of "hypothesis testing" , you must assume the two functions are generated by the same probabalistic process. Until you state exactly what that process is, it isn't possible to say what sort of statistical test is appropriate.

For example if we assume each table is generated by one realization of the uniform [0,1] random variable u according to the formula [itex] f(i,j) = (-0.5)(i) - (0.5)(j) + u [/itex] then it wouldn't make sense to do a chi square test.
 

1. What is a test of two distributions/functions?

A test of two distributions/functions is a statistical technique used to determine whether two sets of data or functions come from the same population or have the same underlying distribution. This is done by comparing the means, variances, or other parameters of the two sets of data or functions.

2. When is a test of two distributions/functions used?

A test of two distributions/functions is used when there is a need to compare two data sets or functions and see if they are significantly different from each other. This could be in the context of a scientific experiment, market research, or any other situation where data needs to be analyzed and compared.

3. What are the assumptions made in a test of two distributions/functions?

The main assumptions made in a test of two distributions/functions are that the data is normally distributed, the samples are independent, and the variances of the two populations are equal. If these assumptions are not met, alternative tests may need to be used.

4. What are the types of tests used for comparing two distributions/functions?

There are different types of tests that can be used to compare two distributions/functions, depending on the nature of the data and the research question. Some common tests include the t-test, ANOVA, and chi-squared test. It is important to choose the appropriate test based on the type of data and research question.

5. How do you interpret the results of a test of two distributions/functions?

The results of a test of two distributions/functions will provide a p-value, which indicates the probability of obtaining the observed results if the two distributions/functions were actually the same. If the p-value is less than a pre-determined significance level (usually 0.05), it is considered statistically significant and the null hypothesis (that the two distributions/functions are the same) can be rejected. If the p-value is greater than the significance level, there is not enough evidence to reject the null hypothesis.

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