What potential pitfalls should be considered when trying to generate numbers that mimic a Gaussian function in PHP?

When trying to generate numbers that mimic a Gaussian function in PHP, one potential pitfall to consider is the accuracy of the generated numbers. Using a simple random number generator may not produce numbers that closely resemble a Gaussian distribution. To address this, you can use the Box-Muller transform to generate Gaussian-distributed random numbers in PHP, which involves generating pairs of uniformly distributed random numbers and transforming them into Gaussian-distributed numbers.

function gaussianRand() {
    static $rand2 = null;
    $generate = false;

    if ($rand2 === null) {
        $generate = true;
    } else {
        $rand1 = $rand2;
        $rand2 = null;
    }

    if ($generate) {
        $x1 = mt_rand() / mt_getrandmax();
        $x2 = mt_rand() / mt_getrandmax();
        $rand1 = sqrt(-2 * log($x1)) * cos(2 * M_PI * $x2);
        $rand2 = sqrt(-2 * log($x1)) * sin(2 * M_PI * $x2);
    }

    return $rand1;
}

// Generate Gaussian-distributed random numbers
$gaussianNumber = gaussianRand();
echo $gaussianNumber;