P-value, significance level and hypothesis

Question

I am confused about the concept of p-value. In general, if the p-value is greater than alpha which is generally 0.05, we are fail to reject null hypothesis and if the p-value is less than alpha, we reject null hypothesis. As I understand, if the p-value is greater than alpha, difference between two group is just coming from sampling error or by chance.So far everything is okay. However, if the p-value is less than alpha, the result is statistically significant, I was supposing it to be statistically nonsignificant ( because, in case p-value is less than alpha we reject null hypothesis).

Basically, if result statistically significant, reject null hypothesis. But, how a hypothesis can be rejected, if it is statistically significant? From the word of "statistically significant", I am understanding that the result is good.

Albumin

try maybe here: stats.stackexchange.com – Heterodyne

Answer 1

You are mistaking what the significance means in terms of the p-value.

I will try to explain below:

Let's assume a test about the means of two populations being equal. We will perform a t-test to test that by drawing one sample from each population and calculating the p-value.

The null hypothesis and the alternative:

H0: m1 - m2  = 0
H1: m1 - m2 != 0 

Which is a two-tailed test (although not important for this).

Let's assume that you get a p-value of 0.01 and your alpha is 0.05. The p-value is the probability of the means being equal when sampling from the two populations (m1 and m2). This means that there is a 1% probability that the means will be equal or in other words only 1 out of 100 sample pairs will have a mean difference of 0.

Such a low probability of the two means being equal makes us confident (makes us certain) that the means of the populations are not equal and thus we consider the result to be statistically significant.

What is the threshold that makes us think that a result is significant? That is determined by the significance level (a) which in this case is 5%.

The p-value being less than the significance level is what makes us think that the result is significant and therefore we are certain that we can reject the null hypothesis since the probability of the NULL hypothesis being true is very low.

I hope that makes sense now!

Dekameter

Happy to have helped :) – Dekameter

The p-value is not the probability that the null hypothesis is true. It is the probability that values beyond the threshold would be attained when given the null hypothesis is true. – Usn

@Usn Thanks. I used the wrong wording in that sentence. Sometimes, when I try to explain a statistical concept to someone in plain English, I focus more on the explanation and sometimes I say something that is not quite right statistically. The concept was explained correctly in the next sentence though. I updated the answer. – Dekameter

Answer 2

Let me make an example that I often use with my pupils, in order to explan the concepts of null hypothesis, alpha, & significance.

Let's say we're playing a round of Poker. I deal the cards & we make our bets. Hey, lucky me! I got a flush on my first hand. You curse your luck and we deal again. I get another flush and win. Another round, and again, I get 4 aces: at this point you kick the table and call me a cheater: "this is bs! You're trying to rob me!"

Let's explain this in terms of probability: There is a possibility associated with getting a flush on the first hand: anyone can get lucky. There's a smaller probability of getting too lucky twice in a row. There is finally a probability of getting really lucky three times in a row. But for the third shot, you are stating: "the probability that you get SO LUCKY is TOO SMALL. I REJECT the idea that you're just lucky. I'm calling you a cheater". That is, you rejected the null hypothesis (the hypothesis that nothing is going on!)

The null hypothesis is, in all cases: "This thing we are observing is an effect of randomness". In our example, the null hypothesis states: "I'm just getting all these good hands one after the other, because i'm lucky"

p-value is the value associated with an event, given that it happens randomly. You can calculate the odds of getting good hands in poker after properly shuffling the deck. Or for example: if I toss a fair coin 20 times, the odss that I get 20 heads in a row is 1/(2^20) = 0.000000953 (really small). That's the p-value for 20 heads in a row, tossing 20 times.

"Statistically significant", means "This event seems to be weird. It has a really tiny probability of happening by chance. So, i'll reject the null hypothesis."

Alpha, or critical p-value, is the magic point where you "kick the table", and reject the null hypothesis. In experimental applications, you define this in advance (alpha=0.05, e.g.) In our poker example, you can call me a cheater after three lucky hands, or after 10 out of 12, and so on. It's a threshold of probability.

Leeland

Answer 3

okay for p-value you should at least know about null hypothesis and alternate hypothesis null hypothesis means take an example we have 2 flowers and it is saying there is no significant difference between them and alternate hypothesis is saying that there is significant difference between them

and yes what is the significant value for p- value most of the data scientist take as 0.05 but it is based on researches(value of level of significant) 0.5 0.05 0.01 0.001 can be taken as p-value

okay now p-value is taken by you but what to do next

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if your model p-value is 0.03 and significant value you have taken 0.05 so you have to reject null hypothesis means there is significant difference between 2 flowers or simple as stated

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p-value of your model < level of significant than reject it

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and your model p-value is >level of significant than null hypothesis is going to accept.

Gratify