You could exclude Hudds and Saints because it makes about as much sense as excluding Quins.
I asked for a better reason to exclude Quins than "in your opinion" you invented a couple including "Look at the chart. Look at the banding of 10 clubs within the two lines showing £1m and -£1m, also +/- £1m around the median." Which proves absolutely nothing. "Look at the chart" FFS!
Then you stumbled upon Grubbs and decided it solved your problem, you don't know what it's for or how to use it correctly do you? Oh hang on I forgot!
Quote in your opinion="Mrs Barista"I do commercial finance for a living. I do it pretty well and get paid pretty well for doing it. One of the key things I look at is relative performance, whether by geography, channel, brand, category, you name it, and I define control groups on all these dimensions on a daily basis. I can tell you now that from a performance review perspective I'd be discredited if I reported a benchmark that included an outlier of this magnitude"'"
Quote in your opinion="Mrs Barista"Grubbs is my new favourite.'"
Are you seriously suggesting that Quins are not a part of the data set SL clubs? Are you saying that they do not fall within +- 3 standard deviations of the Mean? Exactly where we would expect the vast majority of clubs results to fall?
If you are, I hope your boss doesn't know your user name on here.
On average every club was losing £500K that is what an average tells us, the average result for the whole data set. There is likely no single result that exactly matches the average, some will be higher and some lower. Some may even be a lot lower but that does not mean they should not be included regardless of whose opinion it is.
On a like for like basis (i.e. not excluding any given club for spurious and statistically false reasons) if the average is less of a loss this year compared to last, then of course (bearing in mind the many ways financial results can be massaged) that is good news.
What we can't do is compare an average using a spurious and statistically incompetent method this year against an average calculated in a different way for last year.
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