No, it is not.
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Data is statistically significant if the p (probability) value is below a certain level (ex: 5% or 1%). The p value describes how often one would receive the results they got if left to chance alone. The lower the p value, the less likely it is that your results were due to chance and is stronger evidence against the null hypothesis. Also important to keep in mind is that just because something is statistically significant does not mean it is practically significant.
There is an established statistical point for most comparisons or measurements that is so small that differences at or below it are considered to be "random", "predictable", or "meaningless". If a difference between A and B exceeds this point, it is said to be "significant", which does not necessarily mean "important" or "huge" - just "significant".
It is the likelihood of any particular event occurring.
The term statistically valid means a study is able to draw conclusions that are in agreement with statistical and scientific laws. This relies on mathematical and statistical laws.
The F distribution is a function defined over non-negative real numbers, and it takes all sorts of values over that domain. In isolation, none of the values mean anything. An F-test, is a test based on the ratio of two variances from [approximately] normal distributions and a full interpretation requires information about the degrees of freedom. The degrees of freedom determine how much greater than 1 the value of the F-statistics can be before the result is statistically significant. However, a value near 1, such as this will not be statistically significant.