What statistician is this referring to? Certainly not one who understands probabilities. The first number has nothing to do with it. You tested positive, and there’s only a 3% chance that result is wrong. Time to settle your affairs.

In a sample of 1 million people, 1 person will have the disease, 30,000 however will test positive for having the disease. Notice how the false positives count is way higher than the actual positive count.

Is 97% accuracy rate the same as a 3% false positive rate? It might be a combination of false positive and false negative rate.

Accuracy is defined in relation to a specific population or dataset with a specific rate of disease, not for any individual. To properly characterize the test, you need to know the specificity and sensitivity, and together they tell you how a test will perform on an individual and how much an individual’s pre-test probability increases in the case of a positive test or decreases based on a negative test.

Don’t worry if it’s confusing, Baysean statistics is often counter-intuitive.

If you’re interested, here is a very good 3Blue1Brown video that explains the concept very well.