Hello,

Can you please help me to understand this definition of p - value?

“It is the likelihood of the test statistics being higher than the computed test statics value assuming null hypothesis is true.”

Thanks in anticipation

Hello,

Can you please help me to understand this definition of p - value?

“It is the likelihood of the test statistics being higher than the computed test statics value assuming null hypothesis is true.”

Thanks in anticipation

P value small is same as T-stat big - reject null.

so a p value of 0.01 when hypothesis is 95% one tail - reject null. as 0.01 < 0.05.

Minimum (Maximum?) value you can reject the null hypothesis.

simply, it’s the lowest level of significance at which you can reject the null hypothesis, that said;

- If the chosen α > p, you reject the null
- If the chosen α < p, you don’t reject the null

Ya ,I agree with your point , but still not able to understand the relative comparison of T stats with itself, in the statement I originally posted.

But to best of my knowlede for p-value we conside 2-Tail test.

This is technically incorrect. It it the probability of a test statistic ** equal to or more extreme than** the current one, if the null hypothesis is true-- small point.

The statement in bold isn’t *really* the definition of a p-value (despite that many people say it is-- it’s just a statment about how it could be used-- more operational and poorly simplified-- and, it should ** never** be used in this manner. Alpha should

Your decision rules are correct, though, if you say that a p-value less than *or equal to* alpha will cause a rejection of Ho.

Could you be more specific with your question? Do you mean you are unsure how the p-value and alpha relate to test statistics and critical values?

In most regression output you will generate, the default is to provide a two-tailed p-value, so you would need to divide it by two (possibly need other manipulations depending on the situation) if you wanted to use it for a one-tailed test (say, bi <0). Aside from that, p-values can refer to the probability in one or two tails, up to and inclusive of the current value.

I wrote an article on *p* vs. *α* that explains this fairly well: it has pictures. You’ll find it here: http://www.financialexamhelp123.com/p-vs-α/

Full disclosure: as of 4/25 I’ve installed the subscription software on my website, so there’s a charge for viewing the articles.

Well I know the relationship between alpha and p value…

if alpha is greater than p then reject the null and if it is less than p dont reject the null.

So as per my orginal question : assuming null hypothesis is true , then alpha will be less than p-value.

so t stat calculated using alpha as significance level will give us small t stats as comparison to if, I would have used p-value as my significance level.

I think that is what the original statement posted by me meant to be??

Thanks

This isn’t true.

You could make a Type I error.