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Types of Questions

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Types of Questions

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  1. FAQ: How do I correctly pose a comparison investigative question for Kiwi Kapers 3 and state my conclusions when making an inference about the population?On comparison questions:Level 6 I wonder if heights of NZ Yr 11 boys tend to be greater than heights of NZ year 11 girls (looking for a tendency, do the boxes overlap or not, if they do is it too much, level 6)Level 7 I wonder if the median height of NZ year 11 boys is greater than the median height of NZ year 11 girls (seeing if the informal confidence intervals overlap or not)Level 8 I wonder what the difference in median heights is between NZ year 11 boys and NZ year 11 girls? (or something like that, finding an interval for the difference – like a summary situation. If 0 in the interval then they cannot make the call. They could start with the level 7 question, but use the level 8 idea to answer the question).I am not that keen that they look at differences at level 7. It is more important that they are now using a parameter (the median) and still looking at tendency, i.e. using an informal confidence interval and if they overlap, they cannot make the call.

  2. Types of Questions • Critical Question – if the answer to a question may alter people’s behaviour then it is worth asking. • The ‘nice-to-know’ Question – If you would like to know the answer but it is not going to change behaviour then it is a secondary question. • The ‘Already- answered’ question – often a little research will show that the question you are considering asking has been asked already and adequately answered.

  3. Dealing with Outliers • Outliers that are clearly mistakes should be removed • Extreme values (outliers) will effect the mean much more than the median so that is one good reason we will be using box plots to compare data.

  4. Why not delete all outliers? • What appears to be an outlier may not be abnormal to the population. • Gaps may appear in the data set but this may be due to the size of the data set. • E.g. Page 6

  5. What should I do if a value looks like an outlier, but may not be unusual in the population?

  6. Page 129 Ex 6.01

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