How to convert p value of ANOVA into a range of 0 to 1 like Cramer's V? I was looking for a way to convert the p-values returned by a hypothesis test.
In the general scenario, we consider p < 0.05 to reject the null hypothesis and more than 0.05 to fail to reject it. But when it comes to communicating with the end user, it is very important to "somehow" translate such values into the range of 0 to 1.
I've come across a technique called Cramer's V which translates the Chi-square statistic into 0 (corresponding to no association between the variables) to 1 (complete association) | from reference.

I might have misunderstood it completely, but if it is true, then I am looking for a similar way to translate the result of ANOVA and also the T-test (if they both have different techniques) into 0 to 1.
I would appreciate your help.

Tests of significance (p-values) and effect size statistics (like Cramer's V and eta-squared) measure different things.
For a given sample size and statistical test, they are related.
But across experiments, you can have a small effect size statistic and a small p-value, or vice-versa. They simply measure different things.
Here is an example with a linear model (anova) and eta-squared.  Note that for a one-way anova, eta-squared is the same as the r-squared for the model.  Also note that for this model, the partial eta-squared is the same as eta-squared.
In the first example, the model has two Groups, A and B.  The eta-squared is 0.6 and the p-value is 0.07.
if(!require(DescTools)){install.packages("DescTools")}

A = c(1,2,3)
B = c(3,4,5)

Y = c(A, B)
Group = c(rep("A", length(A)), rep("B", length(B)))

Data1 = data.frame(Group, Y)

model1 = lm(Y ~ Group, data=Data1)

library(DescTools)

EtaSq(model1, anova=TRUE)

   ###           eta.sq eta.sq.part SS df MS  F        p
   ### Group        0.6         0.6  6  1  6  6 0.070484

For the second example, we'll keep the values for Groups A and B the same, but increase the sample size.  Here, the eta-squared is the same (0.6), but the p-value decreases to 0.00016.
A = c(1,2,3,1,2,3,1,2,3)
B = c(3,4,5,3,4,5,3,4,5)

Y = c(A, B)
Group = c(rep("A", length(A)), rep("B", length(B)))

Data2 = data.frame(Group, Y)

model2 = lm(Y ~ Group, data=Data2)

library(DescTools)

EtaSq(model2, anova=TRUE)

   ###           eta.sq eta.sq.part SS df    MS  F            p
   ### Group        0.6         0.6 18  1 18.00 24 0.0001605342

However, if we modify the second example to make the effect size larger, with the same sample size, eta-squared increases (to 0.77) and the p-value decreases (to 0.0000016).
A = c(1,2,3,1,2,3,1,2,3)
B = c(4,5,6,4,5,6,4,5,6)

Y = c(A, B)
Group = c(rep("A", length(A)), rep("B", length(B)))

Data3 = data.frame(Group, Y)

model3 = lm(Y ~ Group, data=Data3)

library(DescTools)

EtaSq(model3, anova=TRUE)

   ###              eta.sq eta.sq.part   SS df    MS  F            p
   ### Group     0.7714286   0.7714286 40.5  1 40.50 54 1.639535e-06

