In statistics, in (P < 0.001), what does the P signify? (comparison of two groups)?

Question

I know what the t-test and q-test are. But I don't know what they mean by p. here's the complete text: "The time to complete resolution of symptoms was significantly shorter in the zinc group than in the placebo group (median, 4.4 days compared with 7.6 days; P < 0.001)."

So, I believe that what abe lincoln, below, is saying is that if the null result was POSSIBLE, not not likely, that if we ran 1000 experiments, then only 1/1000 of those experiments would result in the null being true. thank you! (in many ways, i think this implies that the P value is 1-(the confidence interval); therefore a confidence interval of 99.9 would produce a P value of .001) do you agree?

correction: that was supposed to be "result was POSSIBLE, but not likely, that"

and the 99.9 is in %

Answer 1

The traditional approach to reporting a result requires you to say whether it is statistically significant. You are supposed to do it by generating a p value from a test statistic. You then indicate a significant result with "p<0.05". So let's find out what this p is, what's special about 0.05, and when to use p. I'll also deal with the related topics of one-tailed vs two-tailed tests, and hypothesis testing.
What is a P Value?
It's difficult, this one. P is short for probability: the probability of getting something more extreme than your result, when there is no effect in the population. Bizarre! And what's this got to do with statistical significance? Let's see.read more....http://sportsci.org/resource/stats/pvalues.html

Answer 2

The p value is the probability measure which supports the null hypothesis. But what does this mean?

In statistics they use the 'rare event model' to help answer a question.

The null in this case is that the placebo group and the zinc group would have the same median value.

The p value is based on the belief in the null as being true, and essentially means that if the null were true and we ran the experiment over and over we could see results as extreme in difference in medians like this , but we would see it in less than 1/1000 experiments.

So, having run this experiment you are faced with the choice to decide you are seeing a rare event or the null is not true.

Answer 3

The p-value is the probability something occurred by chance. When you set up your experiment, you set an alpha value for your statistical objective: the how often you're willing to say something happenned when it didn't. If you set your alpha at 0.05, you say that 5% of the time you will accept your hypothesis but be wrong (because it happenned by chance). The lower the p-value, the less likely something happenned by chance. If the p-value is smaller than alpha, you accept your hypothesis.

Answer 4

P signifies significance. In statistics, typically anything of P<0.05 is significant, (results of an experiment support the hypothesis).