What is p-value <2.2e-16?
Please refer to the definition of p-value.The p-value is defined as the probability of obtaining a result equal to or "more extreme" than what was actually observed, when the null hypothesis is true.Smaller P value means the probability of getting the extreme case was lower. I believe 2.2e-16 is just the smallest possible number the system can show, it is basically zero.
Finding the P-value : statistics?
We wish to test Ho: p=0.1 versus Ha: p ≠ 0.1. Our sampled proportion = ^p = 68/400 = 0.17 Since n*po = 400*(0.1) and n*(1-po) = 400*(1-0.1) are both at least greater than 5, then n is considered to be large and hence the sampling distribution of ^p will be normally distributed and follow the z distribution. Based on sample data, our observed value of the z statistic = z = (^p-p0)/√[p0*(1-p0)/n] = (0.17-0.1)/√[0.1*(1-0.1)/400] = 4.67 Our critical value = z(alpha/2) = z(0.05/2) = z0.025 = 1.96 P- value = Twice the area to the right of |z| under the z curve = 2*P(z>4.67) = 2*[P(z>0)-P(0
What does it mean when the p-value is p=0.59 or P=0.027? Which is the preferable p-value you want?
The P value is not the chance that your hypothesis is incorrect, although many people think that. The P value is the probability that you would get results at least as extreme assuming your hypothesis is incorrect.For example, if you were trying to decide if a coin was biased (has a more than 50% probability if showing “Heads” when tossed), you could flip it 100 times and count the number of heads. Let’s say you got 57 heads in that process.Assuming that the coin was fair (the “null hypothesis”, 50% probability of heads), you could calculate that the probability of getting 57 heads or more is p=0.089.I say it isn’t the chance that your hypothesis is incorrect in large part because your hypothesis didn’t enter into the calculation at all. So strictly speaking, the p-value of an experiment does not say anything about your hypothesis.A p-value of [math]p<0.05[/math[ is generally, in many fields, considered to be “significant”, which means that if the hypothesis is false, one in twenty similar experiments will show the same result. You can look at the XKCD comic xkcd: Significant to get an example of what that means. You can also look at the XKCD comic xkcd: P-Values to get an idea as to how p-values are interpreted in the real world.
Can you help me with a p-value question?
Researchers concluded from a randomized experiment that “there was no statistically significant difference in treatments at the 0.05 level.” Which of the following does that imply about the p-value? please help Answers: 1.The p-value was less than 0.05 2.The p-value was less than or equal to 0.95 3.The p-value was greater than 0.95 4.The p-value was greater than or equal to 0.05
P-Value What does it mean!?
A really small p-value means there's convincing evidence that the data means something. Say you had data suggesting a disease (event Y) is related to drinking (variable X). It's still possible that you just ran into drinkers who happen to have that disease, by coincidence. But if your data has p = 0.05, it means the chance of such coincidence is only 5%. You are "95% confident" of this X-Y relationship. p <= 0.05 is said to be "statistically significant" on a 95% confidence level. Used quite commonly. P-value is seen in hypothesis tests and regressions. If you have a multi-variable regression Y = f(x1, x2) i.e. Chance of a disease is affected by drinking and smoking, you have a p-value for each variable. My explanation may not be the most precise (criteria being 1-tail or 2-tail distribution affects p). A stats text can give you more complete definition.
Find the P-value for the indicated hypothesis test?
A medical school claims that more than 28% of its students plan to go into general practive. It is found that among a random sample of 130 of the school's students, 32% of them plan to go into general practice. Find the P-value for a test of the school's claims.
P-Value Problem?
Hypothesis Test for mean: assuming you have a large enough sample such that the central limit theorem holds and the mean is normally distributed then to test the null hypothesis H0: μ = Δ find the test statistic z = (xBar - Δ) / (sx / sqrt(n)) where xbar is the sample average sx is the sample standard deviation n is the sample size The p-value of the test is the area under the normal curve that is in agreement with the alternate hypothesis. H1: μ > Δ; p-value is the area to the right of z H1: μ < Δ; p-value is the area to the left of z H1: μ ≠ Δ; p-value is the area in the tails greater than |z| with a test statistic of z = 2.56 we have a p-value of : 2*P(Z > 2.56) = 0.0104
What is the p value of this answer?
The p-value of the test is the area under the normal curve that is in agreement with the alternate hypothesis. H1a: μ > Δ; p-value is the area to the right of z H1b: μ < Δ; p-value is the area to the left of z H1c: μ ≠ Δ; p-value is the area in the tails greater than |z| a) p-value = 1 - ɸ(z) = 1 - P(Z < z) = P( Z > z) = 0.04163678 b) p-value = ɸ(z) = P(Z < z) = 0.9583632 c) p-value = P(|Z| > z) = P(Z < - z) + P( Z > z) = 2 * P(Z < -z) = 2 * ɸ(-z) = 0.08327356
Find the appropriate p-value to test the null hypothesis, H0: p1 = p2, using a significance level of 0.05.?
Large Sample Hypothesis Test for the Difference in Proportions Let X be the number of success in nx independent and identically distributed Bernoulli trials, i.e., X ~ Binomial(nx, px) Let Y be the number of success in ny independent and identically distributed Bernoulli trials, i.e., Y ~ Binomial(ny, py) Let pxHat = X / nx pyHat = Y / ny pHat = (X + Y) / (ny + ny) Assuming that (nx + ny)*pHat > 10 and (nx + ny)*(1-pHat) > 10 (some will say the necessary condition here is > 5, I prefer this more conservative assumption so that the approximations in the tail of the distribution are more accurate), then to test the null hypothesis H0: px - py = Δ or H0: px - py = Δ or H0: px - py = Δ Find the test statistic z = ((pxHat - pyHat) - Δ ) / (sqrt(pHat * (1 - pHat) * (1/nx + 1/ny)) The p-value of the test is the area under the normal curve that is in agreement with the alternate hypothesis. H1: px - py > Δ; p-value is the area to the right of z H1: px - py < Δ; p-value is the area to the left of z H1: px - py ≠ Δ; p-value is the area in the tails greater than |z| If the p-value is less than or equal to the significance level α, i.e., p-value ≤ α, then we reject the null hypothesis and conclude the alternate hypothesis is true. If the p-value is greater than the significance level, i.e., p-value > α, then we fail to reject the null hypothesis and conclude that the null is plausible. Note that we can conclude the alternate is true, but we cannot conclude the null is true, only that it is plausible. The hypothesis test in this question is: H0: px - py = 0 vs. H1: px - py ≠ 0 The test statistic is: z = (( 0.055 - 0.08 ) - 0 ) / ( √ ( 0.06333333 * (1 - 0.06333333 ) * ( 1 / 200 + 1 / 100 ) z = -0.8380804 The p-value = P( Z > |z| ) = P( Z < -0.8380804 ) + P( Z > 0.8380804 ) = 2 * P( Z < -0.8380804 ) = 0.4019856 Since the p-value is greater than the significance level of 0.05 we fail to reject the null hypothesis and conclude px - py = 0 is plausible.
How can you tell if an academic paper has done p-value fishing?
Normally in a field this thick with such expert answers, I'm just glad to have the chance to learn from you guys. But in this one case, I think my outsider's view might be marginally additive:We now possess the computing resources to fully document all research processes. We should no longer have to rely on forensics to determine the methodological rigor of any study or the honesty and accuracy of any reporting on a study's results.I'm not saying we should all carve out hundreds of hours to actually review video footage and keystroke documentation for every study we examine. I'm saying we should consider that a standard part of any study's documentation, if only because it would greatly accelerate how quickly we all adopt proper methods and would greatly eradicate bad research and bad findings. And when we find something truly remarkable, it would greatly accelerate the acceptance of that finding and aid the recreation of that study by others to verify the results.