Probability that A is smaller than B?

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To determine the probability that data set A is smaller than data set B without assuming a normal distribution, one can use statistical methods like ANOVA, but this only tests for differences in means. If A and B are independent and identically distributed (iid), it can be concluded that the average of A is smaller than or equal to that of B at least 50% of the time. Non-parametric tests, such as the Wilcoxon signed-rank test, can also be applied, although they may require specific conditions regarding the population. The discussion highlights the limitations of making definitive statements about probabilities without additional assumptions or data characteristics. Understanding these statistical principles is crucial for accurate data analysis.
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Given two data sets A and B, we can, say, conduct ANOVA to see if the average is statistically different.

Is there a way to determine what is the probabilty that A is smaller than B?

Let's say that we can NOT assume anything about A and B e.g. if they follow a normal distribution.
 
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wavingerwin said:
Given two data sets A and B, we can, say, conduct ANOVA to see if the average is statistically different.

Is there a way to determine what is the probabilty that A is smaller than B?

Let's say that we can NOT assume anything about A and B e.g. if they follow a normal distribution.

? Do you mean the probability that the average of A is smaller than the average of B? If we know nothing at all, then we can conclude nothing at all.
 
Hornbein said:
? Do you mean the probability that the average of A is smaller than the average of B? If we know nothing at all, then we can conclude nothing at all.

Yes, something to that effect. Is it possible to say "A is smaller than B X% of the time" ?
 
wavingerwin said:
Yes, something to that effect. Is it possible to say "A is smaller than B X% of the time" ?
If we know that A and B are iid (independent identically distributed) random variables then we can say that avg of A is smaller or equal to avg of B at least 50% of the time.

Proof: Since they are iid, P[ avg(A)<= avg(B) ] = P[avg(B) <= avg(A)].

P[ avg(A)<= avg(B) ] + P[avg(B) <= avg(A)] >=1

P[ avg(A)<= avg(B) ] + P[ avg(A)<= avg(B) ] >=1

2P[ avg(A)<= avg(B) ] >=1

P[ avg(A)<= avg(B) ] >=1/2
 
wavingerwin said:
Let's say that we can NOT assume anything about A and B e.g. if they follow a normal distribution.
You can do non-parametric analysis, such as the Wilcox test.
 
I think Wilcoxon signed rank requires the samples be from a single population. Does that fit what we are looking at here?
 
jim mcnamara said:
I think Wilcoxon signed rank requires the samples be from a single population. Does that fit what we are looking at here?
I linked to wrong test. Here's the two sample version.
 
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Much better - thank you. I thought maybe I had lost my last remaining brain cell.
 
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