Linear Discriminant Analysis - Sample size requirements

As general question, Are 70 observations sufficient to design a reliable decision rule for discriminating between two groups, with a Fisher linear discriminant function?
depends how many variables you intend to use for the classification. if the number of variables is much higher than 70, you will most likely go into overfitting
thank for your help.
I have 9 variables.

And if I reduce the number of observations til 30, it will still be reliable?
I would like to understand which is the threshold criteria, I should follow, in order to trust the prediction. As I understand if I use 30 or 70 observations with 9 variables in the model to distinguish between two groups,
If the Manova test highlight a significant difference (p < 0.01) between the groups in the classification function created. The error estimation by bootstrap and Leave-One-Out is more than ok. And an external sample test set gives good prediction. Could still someone argue that the sample size is not enough, under which criteria?
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30 doesn't sound like a sufficient sample size to me...

be very careful of the leave one out and other validation methods, they are sensitive to overfitting. I would split the file into a training set and testing set and make a "blind test".