So, I am confused that 1% being stricter gives low p-value even though high p-value is consider to give us more confidence. Can you guys please explain.

Thanks

So, I am confused that 1% being stricter gives low p-value even though high p-value is consider to give us more confidence. Can you guys please explain.

Thanks

Guys. I am very confused now.

In single sample t-test if our null is that mean is zero. For high p-value like 0.3 we would say we fail to reject null at 5%,1%,etc significance level. Correct?

But for low p-value like 0.012 we reject null at 5% significance level and accept alternative hypothesis? However, we would fail to reject null at 1% significance level when p value is 0.012.

But, 1% significance is considered to be stricter than 5%. So, where is the disconnect. Please explain.

Thanks

Edit: I have fixed some of my typos based on later posts here.

In single sample t-test if our null is that mean is zero. For high p-value like 0.3 we would say we fail to reject null at 5%,1%,etc significance level. Correct?

But for low p-value like 0.012 we reject null at 5% significance level and accept alternative hypothesis? However, we would fail to reject null at 1% significance level when p value is 0.012.

But, 1% significance is considered to be stricter than 5%. So, where is the disconnect. Please explain.

Thanks

Edit: I have fixed some of my typos based on later posts here.

Last edited:

Guys. I am very confused now.

In single sample t-test if our null is that mean is zero. For high p-value like 0.3 we would say we fail to reject null at 5% confidence level. Correct?

But for low p-value like 0.012 we would fail to reject null at 5% confidence level? Please explain.

Thanks

In single sample t-test if our null is that mean is zero. For high p-value like 0.3 we would say we fail to reject null at 5% confidence level. Correct?

But for low p-value like 0.012 we would fail to reject null at 5% confidence level? Please explain.

Thanks

Where you've stated 'confidence level', you mean significance level.

The significance level is the risk that you will make a type 1 error - i.e. say there is an effect where there isn't. So the lower the significance level, the higher your confidence.

A 5% significance level (p=0.05) equates to a 95% confidence interval.

A 1% significance level(p=0.01) equates to a 99% confidence interval.

The lower the p, the higher (and narrower) the confidence interval.