Oh lol at least he isn't the only one who got like 10% on a test. I scored some 10% as well, but I did not average that score (it happened now and then tho). Some regional schools do well, so I think what you say also depends on the school. My school is ranked top 200 so I would say it is around average. I don't know any year 12's and my teacher is new to the school so he doesn't know either. Can you explain what deviation is?

Given there are way more than 400 schools, I feel you can rest easy in knowing you're above average and likely won't have to worry about SAC marks decreasing

Standard deviation is a measure of spread. You're probably aware of mode, median, mean, etc. All of those only really tell you about the one score you're likely to get, they don't really tell you about how far apart or close together the data is. Range tells you the possible variety of values you can get, but doesn't really give you any information about whether there's lots of data in the middle, or lots at the tail ends, or lots in the high values. Interquartile range (IQR) is a little bit better, but can be unreliable for comparing data sets that aren't symmetrical or skewed. That's where standard deviation comes in - standard deviation is related to how far away the data is on average from the mean. If most of the data is really far away from the mean, you have a high standard deviation. If most of the data is really close to the mean, you have a low standard deviation.

The important part for interpreting this data, and why standard deviation (SD) is better than the IQR, is that you can use SD to compare data sets. There are times this doesn't work, but most importantly is that it DOES work when dealing with normally distributed data - and both the study scores and SAC scores (roughly) are normally distributed. The equation to convert the two is:

Where x_1 and x_2 are your two different scores (so in this case, let's say x_1 is the study score and x_2 is the SAC score), mu_1 and mu_2 are your means (so mu_1 is the average of the study scores [which is 30] and mu_2 is the SAC score average [which according to VCAA was 63.6 last year]), and sigma_1 and sigma_2 are your SD (as before - sigma_1 is for the study scores and sigma_2 is for the SACs).

This can get EXTREMELY confusing, but the important bit is to know that you can relate the mean and standard deviation of two different pieces of data fairly accurately. So, the range of data that is [mean - 1*SD,mean] should be about the same for each data set. This is the same for other ranges, such as [mean, mean + 2*SD], or [mean - 2*SD, mean + 2*SD] - take your pick. As long as it's of the form mean + a*SD (where a is any integer), the ranges should be comparable. You'll learn more about it in year 12 probability. In the example I gave, I compared the first example I gave you:

For SACs: [63.6-1*20.2, 63.6]=[43.4, 63.6]

For study scores: [30-1*7, 30]=[23, 30]

This method isn't foolproof, because it doesn't consider how your exam scores will affect everything, BUT it does help you check to see if you're "on-track", so to speak. So, if you're getting a SAC score in the range [43.4, 63.6], then you're "on-track" to get a study score in the range [23, 30].

It's still important to get good exam scores, but you can use the same method to check that your practice exam scores are in the right range if you're really worried. SUPER IMPORTANTLY, don't get complacent, OR psych yourself out, based on this method. This method is nice to make sure you're doing okay, BUT the grade distribution changes every year. The SAC distribution is normally somewhat stable, but anything can happen.