How to understand p-value?

A p-value, in plain English, is a measure of the strength of evidence against a null hypothesis. A smaller p-value means relationship is more likely to be genuine and not just due to chance.
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What is a good value of p-value?

A P-value less than 0.05 is deemed to be statistically significant, meaning the null hypothesis should be rejected in such a case. A P-Value greater than 0.05 is not considered to be statistically significant, meaning the null hypothesis should not be rejected.
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How do you interpret the p-value?

A p-value measures the probability of obtaining the observed results, assuming that the null hypothesis is true. The lower the p-value, the greater the statistical significance of the observed difference. A p-value of 0.05 or lower is generally considered statistically significant.
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When to use 0.01 and 0.05 level of significance?

And this is exactly it: When we put it that way, saying that we want the probability (of the null hypothesis being true) — called a p-value — to be less than 5%, we have essentially set the level of significance at 0.05. If we want the probability to be less than 1%, we have set the level of significance at 0.01.
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What does the AP value of 0.05 mean?

What does p-value of 0.05 mean? If your p-value is less than or equal to 0.05 (the significance level), you would conclude that your result is statistically significant. This means the evidence is strong enough to reject the null hypothesis in favor of the alternative hypothesis.
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Statistical Significance, the Null Hypothesis and P-Values Defined & Explained in One Minute

What does p 0.01 mean?

A P-value of 0.01 infers, assuming the postulated null hypothesis is correct, any difference seen (or an even bigger “more extreme” difference) in the observed results would occur 1 in 100 (or 1%) of the times a study was repeated.
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What does p .05 or α .05 actually mean?

Statistical significance is arbitrary – it depends on the threshold, or alpha value, chosen by the researcher. 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.
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Is .01 or .05 more significant?

Setting a significance level allows you to control the likelihood of incorrectly rejecting a true null hypothesis. This makes your results more reliable. 0.05: Indicates a 5% risk of concluding a difference exists when there isn't one. 0.01: Indicates a 1% risk, making it more stringent.
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What is the difference between the .10 .05 and .01 levels of significance?

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.
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How do you report P values?

If p values are reported, follow standard conventions for decimal places: for p values less than 0.001, report as 'p<0.001'; for p values between 0.001 and 0.01, report the value to the nearest thousandth; for p values greater than or equal to 0.01, report the value to the nearest hundredth; and for p values greater ...
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How do you explain p-value to non-technicians?

The p-value is like the strength of the evidence against this defendant. A low p-value is similar to finding clear fingerprints at the scene — it suggests strong evidence against your hypothesis, indicating that your new feature might indeed be making a difference.
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Is 0.001 significant or not?

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".
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Is the p-value of 0.03 significant?

The p-value obtained from the data is judged against the alpha. If alpha=0.05 and p=0.03, then statistical significance is achieved. If alpha=0.01, and p=0.03, statistical significance is not achieved.
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How to interpret p-values?

Being a probability, P can take any value between 0 and 1. Values close to 0 indicate that the observed difference is unlikely to be due to chance, whereas a P value close to 1 suggests no difference between the groups other than due to chance.
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What is considered a bad p-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.
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Is 0.5 a high p-value?

A P-value above 0.5 is considered to be insignificant while anything below 0.05 is considered to be significant and a P-value less than 0.001 is extremely significant.
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Is p-value 0.1 acceptable?

And although 0.5 or below is generally regarded as the threshold for significant results, that doesn't always mean that a test result which falls between 0.05 and 0.1 isn't worth looking at. It just means that the evidence against the null hypothesis is weak.
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How to decide what p-value to use?

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  • When there definitely is an effect, p-values should be less than 0.001 most of the time, AND less than 0.01 almost all the time; p-values larger than 0.10 should be exceedingly rare
  • A result with p < 0.001 should provide a more accurate estimate of the true effect than a result with p = 0.01 or p = 0.05.
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Why is 0.05 the best significance level?

For decades, 0.05 (5%, i.e., 1 of 20) has been conventionally accepted as the threshold to discriminate significant from non-significant results, inappropriately translated into existing from not existing differences or phenomena.
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What does p-value 0.005 mean?

If the p-value is under . 01, results are considered statistically significant and if it's below . 005 they are considered highly statistically significant.
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When would you use the .01 level of significance instead of the .05 level?

Commonly used significance levels are 0.05 and 0.01. A lower significance level (e.g., 0.01) reduces the risk of false positives but may require larger sample sizes. A higher significance level (e.g., 0.05) increases the power to detect genuine effects but also increases the chance of false positives.
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How to calculate p-value?

  1. For a lower-tailed test, the p-value is equal to this probability; p-value = cdf(ts).
  2. For an upper-tailed test, the p-value is equal to one minus this probability; p-value = 1 - cdf(ts).
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How to report p-value in paper?

The preferred method of reporting P-values is to use an exact number, with two or three significant decimal places rather than as a range or category (e.g., NS, p > . 05, or p < . 05).
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What is the difference between 0.05 and 0.01 alpha levels?

For results with a 95 percent level of confidence, the value of alpha is 1 — 0.95 = 0.05. For results with a 99 percent level of confidence, the value of alpha is 1 — 0.99 = 0.01. And in general, for results with a C percent level of confidence, the value of alpha is 1 — C/100.
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How do you calculate the 0.05 level of significance?

To achieve a significance level of 0.05 for a two-sided test, the absolute value of the test statistic (|z|) must be greater than or equal to the critical value 1.96 (which corresponds to the level 0.025 for a one-sided test).
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