Traditionally, the cut-off value to reject the null hypothesis is 0.05, which means that when no difference exists, such an extreme value for the test statistic is expected less than 5% of the time.
What does the p-value have to be to fail to reject the null hypothesis?
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.
Failing to reject a null hypothesis means there is no sufficient evidence for the expected or the observed effect. Today, if scientists had accepted null hypotheses, the discovery of plant viruses or the rediscovery of many extinct species would not have been possible.
With more variables and more comparisons, p-values are usually considered significant at values smaller than 0.05 (often 0.01) to avoid risk of Type-I error. At no time is 0.6 (or even the much closer-to-significant 0.06) ever considered significant.
Since a p-value of 0.045 is less than 10% (0.1) level of significance, there's sufficient evidence to reject the null hypothesis and support the alternative hypothesis.
Statistical Significance, the Null Hypothesis and P-Values Defined & Explained in One Minute
Is p .035 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.
“Statistically significant” just means “the p-value is lower than some chosen threshold.” Conventionally, the threshold chosen is often 0.05. If that's your threshold, then clearly 0.052 is not lower than 0.05, so it's not statistically 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.
For many basic research experiments, as well as clinical trials, we often accept that a P value of < 0.05 means the difference between two groups is “significant.” But P = 0.048 (for example) for a typical t-test really just means there is a 4.8% chance that the outcome you observed between two groups is not actually ...
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.
In null hypothesis testing, this criterion is called α (alpha) and is almost always set to . 05. If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected.
When your p-value is less than or equal to your significance level, you reject the null hypothesis. In other words, smaller p-values are taken as stronger evidence against the null hypothesis. Conversely, when the p-value is greater than your significance level, you fail to reject the null hypothesis.
Fail to reject the null hypothesis: When we fail to reject the null hypothesis, we are delivering a ``not guilty'' verdict. The jury concludes that the evidence is not strong enough to reject the assumption of innocence, so the evidence is too weak to support a guilty verdict.
If our test statistic is: positive and greater than the critical value, then we have sufficient evidence to reject the null hypothesis and accept the alternative hypothesis. positive and lower than or equal to the critical value, we must accept the null hypothesis.
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).
In other words, the lower the p-value, the less compatible the data is to the null hypothesis (i.e. despite both being significant, p = 0.04 is a weaker significance value than p = 0.004 and therefore we would be more confident that the results are 'true' with p = 0.004) if we are confident that all assumptions were ...
Thus, a p-value of 0.168 is evidence against an alternative hypothesis examined with 99% power, compared to the null hypothesis. Figure 1 illustrates that with 99% power even a 'statistically significant' p-value of 0.04 is evidence for of the null-hypothesis.
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.
Because Ha is related to the claim or hunch that motivated our investigation, we state our conclusion in terms of Ha.) At the 10% level, our poll results are statistically significant (P-value = 0.078).
This is always a tricky question. If you set a significance threshold then yeah, p=0.048 is significant and 0.052 is not. My preferred approach is that you should always consider power when interpreting a p-value. For instance, in a study with 80% power, alpha=.
But P values of 0.051 and 0.049 should be interpreted similarly despite the fact that the 0.051 is greater than 0.05 and is therefore not "significant" and that the 0.049 is less than 0.05 and thus is "significant." Reporting actual P values avoids this problem of interpretation.
P-value is 0.035. The significance level is 0.05. The p-value approach for decision is: if the computed p-value is smaller than the defined significance level in the research study, then the decision of the researcher is to reject the null hypothesis.
