{"id":2401,"date":"2019-07-26T06:34:35","date_gmt":"2019-07-26T06:34:35","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2019\/07\/26\/statistical-hypothesis-testing-spinning-the-wheel\/"},"modified":"2019-07-26T06:34:35","modified_gmt":"2019-07-26T06:34:35","slug":"statistical-hypothesis-testing-spinning-the-wheel","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2019\/07\/26\/statistical-hypothesis-testing-spinning-the-wheel\/","title":{"rendered":"Statistical Hypothesis Testing \u2013 Spinning The Wheel"},"content":{"rendered":"

Author: Lee Baker<\/p>\n

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Knowing when and how to choose the right statistical hypothesis test is no mean feat. It can takes years of learning and practice before you get comfortable with it.<\/p>\n

Fortunately, there are ways to shortcut this by having a process, a strategy and a nice, big diagram!<\/p>\n

Here I’m going to give you all three!<\/p>\n<\/p>\n<\/div>\n

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\u200bGetting Started<\/strong><\/span><\/h3>\n<\/div>\n
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\u200bI think everyone responds well to a good visualisation, so \u200bthat’s where we’re going to start.<\/p>\n

I’ve created what I call The Hypothesis Wheel, and here it is making its debut in the world:<\/p>\n<\/p>\n

<\/a><\/p>\n<\/p>\n

Now, there’s a HUGE amount of information in there, and I don’t expect anyone to absorb it all with just a quick glance, so it will<\/u> take you quite a bit of study time to get to grips with it all.<\/p>\n<\/p>\n

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Hypothesis Testing – a 4 Step Strategy<\/strong><\/span><\/h3>\n<\/div>\n
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\u200bWhen making decisions about which hypothesis test to select, you need a plan of action, and here’s my 4 step strategy:<\/p>\n<\/p>\n<\/div>\n

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  1. \u200bDeduce the properties of your outcome variable (aka dependent \u200bor hypothesis variable)<\/span><\/li>\n
  2. \u200bDeduce the properties of your input variable (aka independent or predictor variable)<\/span><\/li>\n
  3. \u200bDeduce the parameters of the relationship
    <\/span><\/li>\n
  4. Look up the statistic on the Hypothesis Wheel
    <\/span><\/li>\n<\/ol>\n<\/p>\n<\/div>\n
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    Steps 1 & 2: Your Variable Properties<\/strong><\/span><\/h3>\n<\/div>\n
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    \u200bAs steps 1 and 2 \u200bare the same thing, \u200byou can do them together. The properties you need to check for your input and outcome variables are:<\/p>\n<\/p>\n<\/div>\n

    \n
      \n
    1. \u200bData Type<\/span><\/li>\n
    2. \u200bDistribution<\/span><\/li>\n
    3. \u200bNumber of Classes<\/span><\/li>\n<\/ol>\n<\/div>\n
      \n

      There are 4 distinct data types that you’ll come across in your research, and they are Ratio (R), Interval (I), Ordinal (O) and Nominal (N), like this:<\/p>\n

      <\/a><\/p>\n

      In terms of the distribution you need to check whether your data (Ratio or Interval data only) are normally-distributed\u00a0 (ND) or non-normally distributed (NND). Actually, all you really need to know is whether they are symmetrical or not – they don’t actually need to be \u200bfull-blown Gaussian distributions to qualify here.<\/p>\n<\/p>\n

      <\/a>\u00a0<\/p>\n

      Finally, you need to check (Ordinal or Nominal only) how many classes (categories) there are in your data. It’s easier to explain what that means by example – the variable Gender has 2 classes; Male and Female, whereas Colour Of The Rainbow has 7 (ROYGBIV). What you really need to know is whether your variable has 2 classes or more than 2.<\/p>\n<\/p>\n

      <\/a><\/p>\n<\/p>\n

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      Step 3: Relationship Parameters<\/strong><\/span><\/h3>\n<\/div>\n
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      \u200bThe relationship parameter you need to know for the Hypothesis Wheel \u200bis which type of analysis are you conducting, univariate or multivariate, like this:<\/p>\n<\/p>\n

      <\/a><\/p>\n<\/p>\n

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      Hypothesis Wheel Colour Codes<\/strong><\/span><\/h4>\n<\/div>\n
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      To help you navigate around the hypothesis wheel I’ve colour coded various parts of it, like this:<\/p>\n<\/p>\n

      <\/a>\u00a0<\/p>\n<\/p>\n

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      Step 4: Look up Your Statistic on the Hypothesis Wheel<\/strong><\/span><\/h3>\n<\/div>\n
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      We always start in the centre with the properties of the hypothesis variable, coloured in purple. There are 3 concentric circles corresponding to Data Type, Distribution and Number of Classes.<\/p>\n

      Spinning further out, in red we have the properties of the predictor variable – again, there are 3 circles for Data Type, Distribution and Number of Classes.<\/p>\n

      Then we have a blue circle for the relationship parameters, which denotes whether our analysis is univariate (UV) or multivariate (MV). When you look closely you’ll see that there are 2 hypothesis wheels, and the larger one contains only univariate hypothesis tests while the smaller one has only the multivariate hypothesis tests.<\/p>\n

      Finally, the outer orange circle tells us which hypothesis test we should choose in any given circumstance.<\/p>\n<\/p>\n

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      Hypothesis Wheel Example<\/strong><\/span><\/h4>\n<\/div>\n
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      Let’s zoom in on a particular example to see how you would use the hypothesis wheel to tell you which univariate test you should use.<\/p>\n

      Let’s say that your hypothesis variable has the following properties:<\/p>\n