How do you know if p-value is strong or weak?

The p-value in a regression model measures the strength of evidence against the null hypothesis, indicating whether the observed data could occur by chance. A low p-value (<0.05) suggests that the coefficient is statistically significant, implying a meaningful association between the variable and the response.
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How do you know if p-value is strong?

A p-value less than 0.05 is typically considered to be statistically significant, in which case the null hypothesis should be rejected. A p-value greater than 0.05 means that deviation from the null hypothesis is not statistically significant, and the null hypothesis is not rejected.
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What determines whether the p-value is high or low?

P-value shows how likely it is that your set of observations could have occurred under the null hypothesis. P-Values are used in statistical hypothesis testing to determine whether to reject the null hypothesis. The smaller the p-value, the stronger the likelihood that you should reject the null hypothesis.
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What does the p-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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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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Statistical Significance, the Null Hypothesis and P-Values Defined & Explained in One Minute

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 does a p-value less than 0.01 mean?

For example, a p-value that is more than 0.05 is considered statistically significant while a figure that is less than 0.01 is viewed as highly statistically significant.
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How to report significant p-value?

P values should be given to two significant figures, unless p<0.0001. For p values between 0.001 and 0.20, please report the value to the nearest thousandth. For p values greater than 0.20, please report the value to the nearest hundredth. For p values less than 0.001, report as 'p<0.001'.
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What does a P value of 0.06 mean?

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.
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How to interpret p-value in t test?

We can work out the chances of the result we have obtained happening by chance. If a p-value reported from a t test is less than 0.05, then that result is said to be statistically significant. If a p-value is greater than 0.05, then the result is insignificant.
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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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What is the p-value in layman's terms?

The P value is defined as the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a result equal to or more extreme than what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to chance.
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What happens if p-value is high?

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.
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Is it better for p-value to be high or low?

In summary, smaller p-values indicate that the observed data is less likely under the null hypothesis, providing stronger evidence against it. Conversely, larger p-values suggest that the data is more compatible with the null hypothesis, indicating weaker evidence against it.
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What is a strong weak evidence p-value?

Interpreting the p-value

Commonly adopted guidelines suggest p < 0.001 as very strong evidence, p < 0.01 as strong evidence, p < 0.05 as moderate evidence, p < 0.1 as weak evidence or a trend, and p ≥ 0.1 as insufficient evidence.
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What do p-values not tell you?

The P values do not tell how 2 groups are different. The degree of difference is referred as 'effect size'. Statistical significance is not equal to scientific significance. Smaller P values do not imply the presence of a more important effect, and larger P values do not imply a lack of importance.
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How to know if p-value is statistically significant?

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.
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Is P 0.07 significant?

If the p-value is less than 0.05, it is judged as “significant,” and if the p-value is greater than 0.05, it is judged as “not significant.” However, since the significance probability is a value set by the researcher according to the circumstances of each study, it does not necessarily have to be 0.05.
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Is p-value 0.36 significant?

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. That's pretty straightforward, right? Below 0.05, significant.
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How do you report an extremely low p-value?

In case of very small p-values, the convention is to write it as p<0.001. Some journals mention this in the author guidelines. For example, the New England Journal of Medicine (NEJM), also states that p-values smaller than 0.001 should be reported as P<0.001.
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Do you report p-value if not significant?

P values should not be listed as not significant (NS) since, for meta-analysis, the actual values are important and not providing exact P values is a form of incomplete reporting. If P>. 01 then the P value should always be expressed to 2 digits whether or not it is significant.
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What is the difference between p-value and significance level?

The p-value represents the strength of evidence against the null hypothesis, while the significance level represents the level of evidence required to reject the null hypothesis. If the p-value is less than the significance level, the null hypothesis is rejected, and the alternative hypothesis is accepted.
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At what p-value do you reject your hypothesis?

In fact, α=0.05 is so common that it typically is implied when no α is specified, and we consider p-values of 0.05 or smaller to be “small” p-values. Then we run the test and calculate a p-value. If p≤α, we reject the null hypothesis in favor of the alternative hypothesis.
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How to interpret t test results?

Higher values of the t-score indicate that a large difference exists between the two sample sets. The smaller the t-value, the more similarity exists between the two sample sets.
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How to reject a null hypothesis?

You can reject a null hypothesis when a p-value is less than or equal to your significance level. The p-value represents the measure of the probability that a certain event would have occurred by random chance. You can calculate p-values based on your data by using the assumption that the null hypothesis is true.
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