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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.<\/p>\nYou’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.<\/p>\n
Discrete data (only in certain categories) can be thought of as either ordered, or unordered. Ordered data has values which can be ‘ranked’ somehow – like cancer stage, weight categories, or Apgar score. Unordered data doesn’t have a ‘flow’ to it; eye colour, gender, subspeciality of doctor.<\/p>\n
The ordinal (ordered) nature of some discrete data can lead you to do some sorts of testing that you can’t do with unordered (categorical) data.<\/p>\n
Continuous data is sometimes Normal, and sometimes it’s not Normal. (I guess we don’t really need to use a capital N, but I tend to use it to put the point across that this is a special Normal not normal normal.)<\/p>\n
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Normally distributed data has a bell shaped distribution (that is, when you plot frequency against value). Data that does not have this shape is ‘non-Normally distributed’. This again has importance – you can do some tests with Normal data that give a more efficient answer than you can with non-Normal data.<\/p>\n
We’ll go into means, deviation and other things that sound a little S&M (including correction and transformation) in a future post.<\/p>\n
– Archi<\/p>\n
(p.s. – please do tag \/ tweet your favourite thing to be addressed in this series to us when you can)<\/p>\n","protected":false},"excerpt":{"rendered":"
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. […]<\/p>\n