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NBME 21 Answers

nbme21/Block 4/Question#40

A study is conducted to assess the normal mean ...

500 Men from a list of patients scheduled to be examined by a urologist

A study is conducted to assess the normal mean ...

500 Men from a list of patients scheduled to be examined by a urologist

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forerofore
to add up, the urologist himself doesn't add or remove accuracy (since this is a blood test), what decreases the accuracy is the fact that in order to be sent to a urologist you probably are sick in the first place (selection bias), so your urea nitrogen is likely to be altered.
+5

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1. Example of inaccurate but highly precise
a. 500 patients seeing a particular doctor for a particular illness
2. Example of accurate but imprecise
a. 10 patients undergo a screening at a mall
3. Both Accurate and precise
a. 500 patients (high precision) undergo a screening (high accuracy ~ no bias or systemic error)
```

`Accuracy`

means the data points are dispersed, but when you take the mean of those points, that mean (“sample mean”) is nearby the population mean (“true mean”). Data points are “more precise” if the dispersion across data points is smaller than some other set of data points (notice how this is a comparison and not an “absolute” statement); `precision`

says nothing about how close the average of the data points are to the “true mean.”

Keep in mind that `accuracy`

and `precision`

are relative descriptors; you can’t say “so-and-so is precise”; no, you can only say “such-and-such is *more precise* than so-and-so” or “so-and-so is *more accurate* than such-and-such.” So, in this case, we can infer that NBME considers “men at the urologist” to have BUNs that are closer to each other (more clustered; more precise; less dispersed) than the BUNs of “men at mall.”

Here’s a nice image:

https://medbullets.com/images/precision-vs-accuracy.jpg

Question says, that a study is done to measure urea nitrogen in men aged 65. What of the following answers provided has INACCURATE and PRECISE measures

I do agree that for a highly precise measurement you need a big group so your standard error of the mean is lower (ie. increasing the study power).

BUT

Age is really important here, since people aged 65 ARE USUALLY SCHEDULED to the urologist , hence a INNACURATE measure would be people not from average age 65 -> men going to a local shopping center. This group does not reflect the average BUN that you would find in a 65 year old male group, so the

ANSWER IS: 500 MEN FROM A LIST OF PATIENTS UNDERGOING A ROUTINE HEALTH SCREENING AT A LOCAL SHOPPING CENTER -> INNACURATE AND PRECISE -> Does not reflect truly the BUN mean of 65 year old males but it does it precisely (with low standard error of mean, less deviation of results)

I hope im right, if im not please explain me why. :( !!

drdoom
Accuracy means the data points are dispersed, but when you take the mean of those points, that mean (“sample mean”) is nearby the population mean (“true mean”). Data points are “more precise” if the dispersion across data points is smaller than some other set of data points (notice how this is a comparison and not an “absolute” statement); precision says nothing about how close the average of the data points are to the “true mean.”
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drdoom
Keep in mind that “accuracy” and “precision” are relative descriptors; you can’t say “so-and-so is precise”; no, you can only say “such-and-such is more precise than so-and-so” or “so-and-so is more accurate than such-and-such.” So, in this case, we can infer that NBME considers “men at the urologist” to have BUNs that are closer to each other (more clustered; more precise; less dispersed) than the BUNs of “men at mall.” Here’s a nice image: https://medbullets.com/images/precision-vs-accuracy.jpg
+

submitted by seagull(355), 2019-05-29T03:25:18Z

Examining patient from a urologist implies Berkson Bias which would skew the population mean of serum urea nitrogen away from the true accurate mean. Then, realize precision is dependent on statistical "Power" which is increased based on the size of the population of the study. (increased precision = increased statistical power). Therefore, an increase in population of a biased group with lead to inaccuracy with high precision.