# hypothesis testing - how do you know when to reject null hypothesis

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#1
Say the significance level is 5%

the probability you get in the binomial distribution is 0.056

0.056 > 0.05 so do you reject the null hypothesis or accept it... how do you know?

I just dont get it
0
1 month ago
#2
(Original post by m.s124)
Say the significance level is 5%

the probability you get in the binomial distribution is 0.056

0.056 > 0.05 so do you reject the null hypothesis or accept it... how do you know?

I just dont get it
Never say 'accept'.

You either reject the null, or not.

If the p-value (which is what you calculated) is below the significance level, then you reject the null hypothesis. Otherwise, nothing happens.

I would recommend you look at some resources if your understanding on this topic is a bit shaky.
1
#3
(Original post by RDKGames)
Never say 'accept'.

You either reject the null, or not.

If the p-value (which is what you calculated) is below the significance level, then you reject the null hypothesis. Otherwise, nothing happens.

I would recommend you look at some resources if your understanding on this topic is a bit shaky.
Thank you I will keep that in mind

if its below the significance level why do you reject it?
Everywhere ive looked it doesnt explain why
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1 month ago
#4
(Original post by m.s124)
Thank you I will keep that in mind

if its below the significance level why do you reject it?
Everywhere ive looked it doesnt explain why
The whole hypothesis testing idea is that the null hypothesis is a well-established statement about the population parameter of some distribution.

If you go ahead and sample enough points from the distribution, and calculate the same parameter for your sample, then you might think that if it's wildly different, then the population parameter must not be what it is currently.

So you ask: how likely is it, that under the assumption that the null hypothesis is true, that you ended up with your sample parameter being what it is?

If this probability is extremely low, i.e. below the level of significance, then you conclude that it is extremely unlikely that you ended up with some awkward samples due to luck. Instead, it suggests that there has been a shift by the population parameter itself away from what it currently thought to be.
Last edited by RDKGames; 1 month ago
1
#5
(Original post by RDKGames)
The whole hypothesis testing idea is that the null hypothesis is a well-established statement about the population parameter of some distribution.

If you go ahead and sample enough points from the distribution, and calculate the same parameter for your sample, then you might think that if it's wildly different, then the population parameter must not be what it is currently.

So you ask: how likely is it, that under the assumption that the null hypothesis is true, that you ended up with your sample parameter being what it is?

If this probability is extremely low, i.e. below the level of significance, then you conclude that it is extremely unlikely that you ended up with some awkward samples due to luck. Instead, it suggests that there has been a shift by the population parameter itself away from what it currently thought to be.
Thank you!
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1 month ago
#6
(Original post by m.s124)
Thank you!
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#7
(Original post by Muttley79)
Thank you!!
0
1 month ago
#8
(Original post by m.s124)
Thank you!!
I have to say I teach stats and I found the explanation given in this thread somewhat confusing!
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1 month ago
#9
(Original post by Muttley79)
I have to say I teach stats and I found the explanation given in this thread somewhat confusing!
Aw, what a shame
1
1 month ago
#10
(Original post by RDKGames)
Aw, what a shame
Look at the Dr Frost resources and see how a teacher would explain it
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#11
(Original post by Muttley79)
I have to say I teach stats and I found the explanation given in this thread somewhat confusing!
I get what you mean but I had to appreciate the effort
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1 month ago
#12
(Original post by m.s124)
I get what you mean but I had to appreciate the effort
Do you understand what the 5% is getting at now?
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