Joint density functions (gaussian)

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The joint probability density function of independent standard Gaussian random variables X and Y is the product of their individual densities. Since both X and Y follow a standard normal distribution N(0, 1), their joint density function is fXX,Y (x, y) = fX(x) * fY(y). This means that the joint density can be expressed as fXX,Y (x, y) = (1/√(2π)) * e^(-x²/2) * (1/√(2π)) * e^(-y²/2). The key takeaway is that for independent continuous random variables, the joint density is simply the multiplication of their respective densities. Understanding this concept is essential for working with joint Gaussian distributions.
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Would just like a hand with this question

If X and Y are independent standard Gaussian random variables (that is, independent N(0, 1) 's ) do the following:
(a) Write down the joint probability density function fXX,Y (x, y) of X and Y .

I know what the gaussian density function looks like. Is it just a matter of multiplying two gaussian distributions together... where u have a σ1 and σ2 (do the same with the mean)
 
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Any time X, Y are independent continuous random variables their joint density is the product of their individual densities - so yes, all you need to do is multiply the individual densities.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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