Consider the normal curve, aka the bell curve, aka the Gaussian distribution. It is used when it should not be and not used when it should be. Truly a sad situation.

We should all be familiar with the normal curve. Measurements of many human characteristics, intelligence, height and so forth follow the normal curve. Its history is quite interesting and you can find more information on Wikipedia

(https://en.wikipedia.org/wiki/Normal_distribution).

Below are some typical “normal” curves:

We should all be familiar with the normal curve. Measurements of many human characteristics, intelligence, height and so forth follow the normal curve. Its history is quite interesting and you can find more information on Wikipedia

(https://en.wikipedia.org/wiki/Normal_distribution).

Below are some typical “normal” curves:

In my opinion, the key thing about the normal curve is that it is symmetric, with equal numbers of examples (whatever you like, such as height, IQ etc.) on both sides of the peak value. The most telling thing about the normal curve is that one of its original names was the law of error, or the law of facility of errors. What does the normal curve have to do with error? A lot. The first documented case of someone understanding that errors in measurement follow the normal curve was given to us by the famous astronomer Galileo Galilei, way back in 1632, in his famous book

Not only astronomical measurements are subject to error. All measurements are subject to error, including those performed in medical laboratories. The problem with medical laboratory measurements is that they are reported as single numbers, for example, your TSH is 1.5 or your potassium is 4.7. These numbers are reported as rock solid, with implied 100% accuracy even though they are subject to error just like astronomical measurements or any other measurement you care to choose.

How should these values be reported? The report should indicate a range of results based on what is called the 95% confidence interval. For example, that TSH of 1.5 should be reported as being 1.4 to 1.6 with 95% confidence, meaning that there is indeed a chance (5% or 1 chance in 20 to be exact) that the actual value is outside of the 95% confidence range. Since in this example the range of values is within the normal (reference) range for TSH you might ask: "so what’s the big deal?" Well suppose the TSH value measured is closer to the upper limit of the normal range, which is 4.5? Let’s say it is measured as 4.7 on the instrument in the lab. Then the 95% confidence interval is 4.4 to 5.0. So your TSH might actually be normal. Nevertheless, when the value is reported as 4.7 and is “flagged,” or identified as “abnormally high,” many doctors will immediately recommend treatment for hypothyroidism, even though your true value might indeed be within the normal range.

So here we have a situation where all quantitative (numerical) lab values should be reported as being within a 95% confidence interval based on routine error calculations following the normal curve rather than as a single misleading number construed to be rock-solid.

Medical laboratories are implying a misleading and potentially harmful precision in their measurements that simply does not exist, and can never exist because all measurements are subject to error. This misleading implied precision in lab work has played a large part in the development of the whole concept of “precision” medicine because both medical practitioners and the general public are led into believing that laboratory measurements are absolutely precise and totally 100% accurate.

You can read more about the normal curve in medical lab testing in my eBook:

In the next post I will discuss an opportunity for implementing “precision medicine” that is being missed.

© 2016 by Ralph Giorno MD

*Dialogue Concerning the Two Chief Systems of the World - Ptolemaic and Copernican*. In that book Galileo discusses errors in astronomical measurements and notes that the errors follow a normal distribution, although he did not state that in so many words.Not only astronomical measurements are subject to error. All measurements are subject to error, including those performed in medical laboratories. The problem with medical laboratory measurements is that they are reported as single numbers, for example, your TSH is 1.5 or your potassium is 4.7. These numbers are reported as rock solid, with implied 100% accuracy even though they are subject to error just like astronomical measurements or any other measurement you care to choose.

How should these values be reported? The report should indicate a range of results based on what is called the 95% confidence interval. For example, that TSH of 1.5 should be reported as being 1.4 to 1.6 with 95% confidence, meaning that there is indeed a chance (5% or 1 chance in 20 to be exact) that the actual value is outside of the 95% confidence range. Since in this example the range of values is within the normal (reference) range for TSH you might ask: "so what’s the big deal?" Well suppose the TSH value measured is closer to the upper limit of the normal range, which is 4.5? Let’s say it is measured as 4.7 on the instrument in the lab. Then the 95% confidence interval is 4.4 to 5.0. So your TSH might actually be normal. Nevertheless, when the value is reported as 4.7 and is “flagged,” or identified as “abnormally high,” many doctors will immediately recommend treatment for hypothyroidism, even though your true value might indeed be within the normal range.

So here we have a situation where all quantitative (numerical) lab values should be reported as being within a 95% confidence interval based on routine error calculations following the normal curve rather than as a single misleading number construed to be rock-solid.

Medical laboratories are implying a misleading and potentially harmful precision in their measurements that simply does not exist, and can never exist because all measurements are subject to error. This misleading implied precision in lab work has played a large part in the development of the whole concept of “precision” medicine because both medical practitioners and the general public are led into believing that laboratory measurements are absolutely precise and totally 100% accurate.

You can read more about the normal curve in medical lab testing in my eBook:

*Blood Trails: Follow your medical lab work from beginning to end with everything that can go wrong in between, plus how doctors misunderstand and misuse blood tests*. (http://www.amazon.com/Blood-Trails-beginning-everything-misunderstand-ebook/dp/B00YZ1XADE?ie=UTF8&keywords=ralph%20giorno&qid=1459175458&ref_=sr_1_1&sr=8-1).In the next post I will discuss an opportunity for implementing “precision medicine” that is being missed.

© 2016 by Ralph Giorno MD