Is 0.0001 a good p-value?
Also very low p-values like p<0.0001 will be rarely encountered, because it would mean that the trial was overpowered and should have had a smaller sample size. It would seem appropriate, therefore, to require investigators to explain such results and to consider rejecting the research involved.
P < 0.001. Most authors refer to statistically significant as P < 0.05 and statistically highly significant as P < 0.001 (less than one in a thousand chance of being wrong). The asterisk system avoids the woolly term "significant".
A p-value of 0.001 indicates that if the null hypothesis tested were indeed true, then there would be a one-in-1,000 chance of observing results at least as extreme. This leads the observer to reject the null hypothesis because either a highly rare data result has been observed or the null hypothesis is incorrect.
This means that the result is statistically 'signi Gcent both the 0.01 and the 0.05 levels neither the 0.01 nor the 0.05 levels.
If the p-value is under . 01, results are considered statistically significant and if it's below . 005 they are considered highly statistically significant.
A p-value of less than 0.05 implies significance and that of less than 0.01 implies high significance. Therefore p=0.0000 implies high significance.
A p value of 0.06 means that there is a probability of 6% of obtaining that result by chance when the treatment has no real effect. Because we set the significance level at 5%, the null hypothesis should not be rejected.
The p-value 0.03 means that there's 3% (probability in percentage) that the result is due to chance — which is not true.
A very small P-value indicates that the null hypothesis is very incompatible with the data that have been collected. However, we cannot say with certainty that the null hypothesis is not true, or that the alternative hypothesis must be true [5].
A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.
How do you interpret p-value?
It measures how compatible your data are with the null hypothesis. How likely is the effect observed in your sample data if the null hypothesis is true? High P values: your data are likely with a true null. Low P values: your data are unlikely with a true null.
If the p-value is less than 0.05, we reject the null hypothesis that there's no difference between the means and conclude that a significant difference does exist. If the p-value is larger than 0.05, we cannot conclude that a significant difference exists.
Conventionally, a p value of <0.05 is taken to indicate statistical significance. This 5% level is, however, an arbitrary minimum and p values should be much smaller, as in the above study (p = 0.006), before they can be considered to provide strong evidence against the null hypothesis.
P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.
However, if you obtain a p-value = . 06, it is not considered significant, therefore you cannot make a claim about the direction of the effect (even though you might have plotted a graph that might suggest there is a positive relationship for example). The same would go is you have obtained a p-value = . 99.
So, when you get a p-value of 0.000, you should compare it to the significance level. Common significance levels include 0.1, 0.05, and 0.01. Since 0.000 is lower than all of these significance levels, we would reject the null hypothesis in each case.
Common significance levels are 0.10 (1 chance in 10), 0.05 (1 chance in 20), and 0.01 (1 chance in 100). The result of a hypothesis test, as has been seen, is that the null hypothesis is either rejected or not. The significance level for the test is set in advance by the researcher in choosing a critical test value.
If the p-value is 0.05 or lower, the result is trumpeted as significant, but if it is higher than 0.05, the result is non-significant and tends to be passed over in silence.
If the number is greater than 0.05, the relationship is not statistically significant. For example, if your value is . 003, then we can be confident that the relationship between the two variables is significant and not as result of random chance.
In this context, what P = 0.04 (i.e., 4%) means is that if the null hypothesis is true and if you perform the study a large number of times and in exactly the same manner, drawing random samples from the population on each occasion, then, on 4% of occasions, you would get the same or greater difference between groups ...
What does a .002 p-value mean?
The smaller the p-value the greater the discrepancy: “If p is between 0.1 and 0.9, there is certainly no reason to suspect the hypothesis tested, but if it is below 0.02, it strongly indicates that the hypothesis fails to account for the entire facts. We should not be off- track if we draw a conventional line at 0.05”.
In all, the description given of X²(1, N=100)=6.83, p =0.009 indicates that the authors ran a chi-squared test with 1 degree of freedom on 100 samples, which yielded a test statistic of 6.83, which is unlikely to have been observed under the null hypothesis (p=0.009)
If a p-value is lower than our significance level, we reject the null hypothesis. If not, we fail to reject the null hypothesis.
[1,2] It has been observed in many articles published in medical journals that if the P value is less than 0.05, the study is considered as positive and if the P value is more that 0.05, it is considered as negative.
The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis. When the p-value falls below the chosen alpha value, then we say the result of the test is statistically significant.
Rejecting the Null Hypothesis
Reject the null hypothesis when the p-value is less than or equal to your significance level. Your sample data favor the alternative hypothesis, which suggests that the effect exists in the population. For a mnemonic device, remember—when the p-value is low, the null must go!
High p-values indicate that your evidence is not strong enough to suggest an effect exists in the population. An effect might exist but it's possible that the effect size is too small, the sample size is too small, or there is too much variability for the hypothesis test to detect it.
In other words, very small p-values suggest that we reject the null hypothesis and instead accept the alternative hypothesis, i.e., that the means are probably different. The smaller the p-value, the more incompatible the data are with the null hypothesis.
If your p-value is less than your selected alpha level (typically 0.05), you reject the null hypothesis in favor of the alternative hypothesis. If the p-value is above your alpha value, you fail to reject the null hypothesis.
For example, if the P value is 0.001, it indicates that if the null hypothesis were indeed true, then there would be only a 1 in 1000 chance of observing data this extreme.
What does p-value of 0.07 mean?
For example, if your data generate a p-value of 0.07 (sometimes termed a 'trend'), the Bayes factor upper bound is 1.98 and you can conclude that the alternative hypothesis is at most twice as likely as the null hypothesis. A p-value of 0.01 indicates the alternative hypothesis is at most 8 times as likely as the null.
For example, a P-value of 0.08, albeit not significant, does not mean 'nil'. There is still an 8% chance that the null hypothesis is true. 7. A P-value alone cannot be used to accept or reject the null hypothesis.
A P-value less than 0.5 is statistically significant, while a value higher than 0.5 indicates the null hypothesis is true; hence it is not statistically significant.
The likelihood chi-square statistic is 11.816 and the p-value = 0.019. Therefore, at a significance level of 0.05, you can conclude that the association between the variables is statistically significant.
A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).
P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.
If the p-value is 0.05 or lower, the result is trumpeted as significant, but if it is higher than 0.05, the result is non-significant and tends to be passed over in silence.
. The lower the p-value is, the lower the probability of getting that result if the null hypothesis were true. A result is said to be statistically significant if it allows us to reject the null hypothesis. All other things being equal, smaller p-values are taken as stronger evidence against the null hypothesis.
The probability to the right of is called the P-value. The P-value is 0.0006, meaning that we would only expect to get this result in 6 out of 10,000 samples. This is very unlikely, so we will reject the null hypothesis in favor of the alternative hypothesis and conclude that the cola actually did lose sweetness.
Below 0.05, significant. Over 0.05, not significant.