The p-value, used in null-hypothesis significance testing, represents the likelihood that the test findings will be at least as extreme as the result actually observed, assuming that the null hypothesis is true.
Therefore, the p-value for study b exists 0.080.
The P value is the likelihood, for a particular statistical model, that the statistical summary would be either equal to or more extreme than the actual observed results if the null hypothesis were to hold.
In a two tailed test the probability of occurrence exists the total area beneath the critical range of values on both the sides of the curve (negative side and positive side)
Therefore, the probability values for a two tailed test as compared to a one tailed test exists given by the under given relation -
[tex]$p-\text { value }=P\left(Z < -\frac{\alpha}{2}\right)+P\left(Z > \frac{\alpha}{2}\right)$[/tex]
simplifying the above equation, we get
[tex]$P \frac{\alpha}{2}=0.040$[/tex]
Substituting the given value in above equation, we get -
Probability values for a two tailed test
= 0.040 + 0.040
= 0.080
Therefore, the p-value for study b exists 0.080.
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