# Collecting Data

Students learn how to design a questionnaire without bias  to collect primary qualitative and quantitative data sets.  As learning progresses they use stratified sampling to determine sample size and how to design two-way tables and frequency trees to organise these data.

This unit takes place in Year 9 Term 4 and follows on from calculating statistical measures.

##### Prerequisite Knowledge
• Interpret and construct statistical diagrams for discrete and continuous data and know their appropriate use.
• interpret, analyse and compare the distributions of data sets from univariate empirical distributions through:
• appropriate graphical representation involving discrete, continuous and grouped data
• appropriate measures of central tendency (median, mean, mode and modal class) and spread
##### Success Criteria
• Infer properties of populations or distributions from a sample, whilst knowing the limitations of sampling.
• apply statistics to describe a population
• Interpret, analyse and compare the distributions of data sets from univariate empirical distributions through appropriate graphical representation involving discrete, continuous and grouped data.
##### Key Concepts
• Students need to understand the benefits of using two-way tables as a means to exhaustively cover each outcome for multiple events and use them to calculate probabilities.
• When designing questionnaires students need to consider time periods, multiple check boxes which do not overlap and the need to collect a wide ranging sample to reduce bias.
• It is important to recognise the different statistical techniques that are used to analyse and represent qualitative, quantitative, discrete and continuous data.
##### Common Misconceptions
• Students often have difficulty designing two-way tables.
• When designing questionnaires common errors include:
• No time period
• Overlapping responses
• Lack of ‘none’ or ‘other’ option.
• Check boxes with unequal widths.
• Double negative questions.
• Students often try to represent continuous data using methods that are only applicable for discrete sets.

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