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“This video. We ll take a look at how to interpret a p value. So. So.
What is a p value p. Value stands for probability value. It indicates. How it is that a result occurred by chance alone.
If the p value is small. It indicates. The result was unlikely to have occurred nby chance alone. These results are known as being statistically significant a small p value means that it s greater than chance alone.
Something happened the test is significant. Whereas. A large p value indicates that the result is within chance or normal sampling error or in other words. Nothing happened.
The test is not significant and p values range from 0. To 1 let s go ahead and take a look at some np values of popular computer output. So here s spss and the p value is given..
Where it says sig. 2. Tailed. And it s very small in this example at 002 now we ll formalize this in a minute.
But for now that s beyond chance. It s quite small so we would say that the result is statistically significant taking a look at another example here. We see a p value reported as sig once again in spss as 268. Now that s not really that small so that s within normal sampling error.
We would say or it s not beyond chance so. The result is not statistically significant now to formalize this we want to look at what is known as alpha. And when we interpret whether a p value is significant that is beyond chance or not we need to know the level of alpha being used for the test. The two most common levels are 05.
For alpha or 01. Alpha is decided beforehand by the researcher or analyst. So if we use an alpha of 05. The following rule applies if p or sig as you saw in our output is less than alpha or less than 05.
In this case. The test is significant whereas if p or sig is greater than 05. The test is not significant so as an example if we had a p value or a sig of 03 using the rule up above with an alpha of 05..
Would the test be significant or not well. The test would be significant nright. Because 03 is less than 05. Let s go ahead.
And take a look at another. Example. So. If we have a p of 12.
Now with alpha 05. Try and assess whether or not that test would be significant. It would not be significant because. 12 is greater than 05.
So it s not significant or it s not beyond sampling error. And here s a table of some different outcomes. So looking at this first row. If we had a p of 04.
And we use alpha 05. In all decisions. This is less than 05..
So we would say it s significant our second value 075. That s greater than 05. Right so we would say that s not significant 049 that s very close. But it is less than 05.
So that would be significant and then 523 that s definitely greater than 05. So it s not significant and then finally 001 definitely less than 05. So this result is significant now if we look at alpha of 01. We use really the same decision rule if p is less than alpha.
This time alpha s 01. Though the test is significant whereas if p is greater than alpha. The test is not significant so if we have an example here with a p of 04. Using an alpha of 01.
We would see that the test is not significant because 04 is greater than 01. If we had an example of 003. We would see that it is significant in this case. Because 003 is less than 01.
So let s go and take a look at some examples as well with alpha 01. So here we have a p of 001. That s definitely less than alpha of 01..
So it s significant 02 it s close. But it s greater than 01. So it s not significant 009 once again quite close. But less than alpha of 01.
So it s significant 523 is greater than 01. So it s not significant and then finally 012 close once again. But it is greater than 01. So it s not significant ok.
Let s look at a p value again in spss. So now we have a p of 002 and it s very small let s assess this for significance using alpha 05. So because 002 is less than 05. The test is significant at alpha 05.
Now before we close ask yourself would it have been significant. If alpha 01. Was used well since 002 is less than 01. The test would be significant.
If an alpha 01. Was used as well ” ..
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