# Statistical test for cross validation with a low mean

I'm working on comparing 2 algorithms with an experimental protocol that produce 100 folds for each one.

As a result, I found that my algorithm got (49.29 $$\pm$$ 1.69) and the baseline got (50.40 $$\pm$$ 2.16). I applied ANOVA and other tests and I always got a p-value of 0.60.

Method: Deep learning.

Goal: comparing 2 algorithms (mine and another)

Field: computer-vision

Hypothesis ($$\alpha=0.05$$):

• $$H_0$$: the mean of the results are equal.
• $$H_a$$: the mean of the results are unequal. (advantage go to the adversary)

Results:

• $$mean_{proposed}$$ < $$mean_{baseline}$$

• Population: 2 ( proposed and baseline)

• sample size = 100

• $$P=0.6$$ and $$\alpha = 0.05$$

• $$P > 0.05$$ $$=>$$ no significant difference

My conclusion : the 2 algorithms are equal.

Can a reviewer reject my conclusion (my fail to reject the H0)?

How can I defend my point of view?