The term "significant" is seductive and easy to misinterpret, because the statistical use of the word has a meaning entirely distinct from its usual meaning. Just because a difference is statistically significant does not mean that it is biologically or clinically important or interesting. Moreover, a result that is not statistically significant (in the first experiment) may turn out to be very important.

Using the conventional definition with alpha=0.05, a result is said to be statistically significant when a difference that large (or larger) would occur less than 5% of the time if the populations were, in fact, identical.

The entire construct of 'hypothesis testing' leading to a conclusion that a result is or is not 'statistically significant' makes sense in situations where you must make a firm decision based on the results of one P value. While this situation occurs in quality control and maybe with clinical trials, it rarely occurs with basic research.

If you do not need to make a decision based on one P value, then there is no need to declare a result "statistically significant" or not. Simply report the P value as a number, without using the term 'statistically significant'. Better, simply report the confidence interval, without a P value.