Well, of course, because that statement is deeply incorrect—the described mistake would cause the most recent reading to have more weight.
If you have a set of readings, say, [0.1, 0.02, 0.3, 0.05, 0.08], normally when you average them you would get 0.55—the mean of the set.
Calculating the average by "averaging the new reading with the previous average" would mean new + old / 2 every time. That means that for each reading after the first, your "averages" would be: [0.06, 0.18, 0.115, 0.195].
If we add a new reading of 0.01 to each of these, in the first case, we would get an average of 0.46, and in the second case, 0.1025. As you can see, even taking into account the already-very-skewed numbers, the second case biases it much further in favor of the new reading (which, in this case, is very low compared to the existing readings).
If you have a set of readings, say, [0.1, 0.02, 0.3, 0.05, 0.08], normally when you average them you would get 0.55—the mean of the set.
Calculating the average by "averaging the new reading with the previous average" would mean new + old / 2 every time. That means that for each reading after the first, your "averages" would be: [0.06, 0.18, 0.115, 0.195].
If we add a new reading of 0.01 to each of these, in the first case, we would get an average of 0.46, and in the second case, 0.1025. As you can see, even taking into account the already-very-skewed numbers, the second case biases it much further in favor of the new reading (which, in this case, is very low compared to the existing readings).