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LMCC score = 483


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^ Assuming a mean of 500 and sd of 100, your score of 483 would give you ~43%ile using z score calculations.

 

Edit: In a week or so when they release the official data for this year's LMCC you can calculate your actual %ile using the mean and sd. Just goggle z score calculation and z score chart.

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^ Assuming a mean of 500 and sd of 100, your score of 483 would give you ~57%ile using z score calculations.

 

Edit: In a week or so when they release the official data for this year's LMCC you can calculate your actual %ile using the mean and sd. Just goggle z score calculation and z score chart.

 

You can't be above 50th percentile if you're below the mean.

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You can always calculate percentiles for any continuous distribution. The median is simply the 50th percentile.

 

How can you calculate a percentile with just a mean and SD if the distribution is not normal? My stat knowledge is kinda rusty but I thought that was not possible.

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How can you calculate a percentile with just a mean and SD if the distribution is not normal? My stat knowledge is kinda rusty but I thought that was not possible.

 

The definition of the p-th percentile is simply whatever value that is greater than or equal to p-percent of the observations. So if 80% of LMCC Part I scores are at or below 600 than we find the 80th percentile at 600.

 

Likewise if the median score is 495 than this is the 50th percentile.

 

The helpful thing about a normal distribution (or observations that can be readily approximated with one) is that we can "standardize" each observation with the observed mean and SD to a "z-score", which is a simply a standard normal (mean 0, SD 1) value which has a standard, known associated percentile.

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The definition of the p-th percentile is simply whatever value that is greater than or equal to p-percent of the observations. So if 80% of LMCC Part I scores are at or below 600 than we find the 80th percentile at 600.

 

Likewise if the median score is 495 than this is the 50th percentile.

 

The helpful thing about a normal distribution (or observations that can be readily approximated with one) is that we can "standardize" each observation with the observed mean and SD to a "z-score", which is a simply a standard normal (mean 0, SD 1) value which has a standard, known associated percentile.

None of that is helpful if the scores aren't normally distributed and if we don't know what that distribution looks like (with the raw data). So again, I don't see how you can calculate percentile based on mean and SD only if you don't have a normal distribution of data.

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You don't calculate any percentiles based on mean and SD directly. Computing z-scores ("normalization") allows for conversion to standard normal values which are then compared to known percentiles for the standard normal distribution. A p-th percentile is defined as

 

p/100 = int (- inf to x) F(a) da

 

where x is the p-th percentile of the cumulative distribution function F. It may also be expressed by F^(-1)(p/100) = x, where F^(-1) represents the inverse cdf. Since for empirical (and, indeed, many theoretical*) distributions there is no closed form of F^(-1), numerical methods are employed to approximate x. The bottom line is that you can always compute the empirical distributions based on random samples and derive descriptive statistics (mean, median, quartiles, percentiles, variance, standard deviation) irrespective of the theoretical "underlying" distribution.

 

*The normal distribution has a closed-form probability distribution function but doesn't have a closed form cdf (the integral of the pdf does not exist).

 

Anyway that's enough mathematical statistics for a Friday night.

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None of that is helpful if the scores aren't normally distributed and if we don't know what that distribution looks like (with the raw data). So again, I don't see how you can calculate percentile based on mean and SD only if you don't have a normal distribution of data.

 

+1 - I'm not sure why people are arguing... the mean and SD are used to calculate a Z score, assuming a normal distribution, and subsequently this can be used to estimate percentiles.

 

There is no easy way that I know of to estimate percentiles of a non-normally distributed sample.

 

That being said, it is probably a relatively safe assumption that the LMCC scores are close enough to being normally distributed that we can use z scores to calculate.

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^ PRECISELY.

 

If you are not given percentiles, you can assume normal distribution and use median/sd.

 

For a large exam such as the MCCQE1, it's almost a given that the results will be close to a normal distribution.

 

so basically this is the ~40th penticle and yet it's still a passing score..?

A pass is set such that something like 2% of CMGs fail, I believe. So 40th percentile is below the mean, but nothing wrong with that.

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There is no easy way that I know of to estimate percentiles of a non-normally distributed sample.

 

It's quite easy to compute descriptive statistics for a sample with an unknown distribution. It is, however, time-consuming without a computer and to do so by hand. We can readily come up with a robust formula to give empirical percentiles and write a little script for a program like R to compute it.

 

I'm not arguing that it isn't normally distributed. This huge tangent all came when I used the word mean when I meant to say median, though the two should equal each other in a normal distribution so it's a moot point.

 

Yes. But I reserve the right to be pedantic about probability. ;)

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Detailed MCCQE Part 1 results are now released.

 

Mean ~= 480

Standard deviation = 100

Pass = 390

 

Assuming a normal distribution means that a passing score is above the 16th percentile.

 

So for the OP, a score of 483 is about the 51st percentile given the assumptions above.

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