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stats

StatsMiniBlog: Continuous vs. Categorical

13 May, 13 | by Bob Phillips

So – in response to a tweet from @DocNadine Archi will be attempting to do a series of short posts on some ‘stats’ things. What would you like to see covered?

A short and simple one today. Continuous vs. Categorical more…

StatsMiniBlog: Order and Normality

15 May, 13 | by Bob Phillips

You’ve cracked the first step with data – you can tell if its continuous or discrete. As you progress to stats nirvana, you need to delve more deeply into the stuff. more…

StatsMiniBlog: Size and variability

22 May, 13 | by Bob Phillips

Now you now know you continuous data can be Normal or not Normal (but we might be able to tweak that … see the next post) and we’d like to be able to describe it clearly and accurately.

We could just reproduce every bit, but we really want to compress it to get the meat & meaning across. more…

StatsMiniBlog: Transformations

29 May, 13 | by Bob Phillips

20140205-091454.jpgThere are a host of things in the world that undergo transformations. These are often physical, emotional, psychological and spiritual. But numbers need love too, and we are getting to know that deep, deep down, we all love Normality. more…

StatsMiniBlog: Parametric? :-/ Like paramedic or paralegal?

12 Jun, 13 | by Bob Phillips

We have – in this microseries of miniblogs – looked at data distributions and describing what we’ve got. We’re ready for the big leap now; from description into inference. What can we say about how our data relate to the world at large? And the first thing to do is to clarify a deeply unhelpful term.

Parametric.

more…

StatsMiniBlog: Standard Error

16 Jun, 13 | by Bob Phillips

This is it – a leap from the descriptive to the inferential. We are leaving the comfort of the sample we have collected data on and we’re about to make a statement that relates to the world beyond: we are inferring stuff.

Annoyingly, this first step is a phrase disturbingly close to another. The ‘standard error of the mean’ (aka ‘standard error’) is an number we can use to estimate how the mean of our sample relates to the mean of the population at large. In order to keep it clearly different in my mind than ‘standard deviation‘ I tend to think of it as ‘standard error of the mean’ and not  just ‘standard error’. more…

StatsMiniBlog: Significance tests. Step one.

26 Jun, 13 | by Bob Phillips

Moving through from  estimating how the mean of a sample might reflect the mean of the population at large, we want to see if there are differences between two (or more) groups. This is usually done by ‘doing a statistical test’. What we won’t be dealing with here is the maths that derives these, the processes of creating the test score and checking this through to an answer, but the core concept. What is it we’re doing when we ‘do a statistical test’

more…

StatsMiniBlog: Significance tests. Step two.

30 Jun, 13 | by Bob Phillips

Now last time we examined the core question of ‘what is a statistical test asking’ with the answer  ‘what’s the likelihood that the results I’ve got from these two groups are different only because of the play of chance?’

Now depending on what sort of data you have will depend on what ‘the results from these two groups’ looks like. It might be that the data are continuous and Normal (so we need parametric tests), or continuous and non-Normal, or categorical (so we need non-parametric tests).
more…

StatsMiniBlog: Significance tests. Step three.

17 Jul, 13 | by Bob Phillips

So, many of you will know that the first rule is that the Doctor lies. The last post might have given you the impression that that was the whole of statistics … but there is a bit more. The first idea that goes beyond the simple question is ‘how are these two continuous variables related to each other’? more…

StatsMiniBlog: Bootstrapping

8 Aug, 13 | by Bob Phillips

StatsMiniBlogAs mentioned in previous posts, part of the joys of playing with numbers is in making inferences about how the future will be. This is often why you’re looking for a confidence interval, showing (sort of) where the truth lies with 95% certainty.

How you build these CI is interesting.

One way is to assume a Normal distribution exists, and then the sample size and sample mean will give you the CI (using the standard error of the mean). If you’re not quite sure that it really is a Normal distribution, then you have a range of opportunities, one of which is a bit 50 shades of starts: bootstrapping. more…

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