data handling and analysis

1. Process of doing statistics
Formulate questions
•Asking and answering questions : not yes/no questions
Collect data
•Design a plan on how to collect the data
Analyze data
•Use methods to analyzed data: graphical, numerical etc
Interpret results
•Interpret the analysis and relate to the question
The processes involve statistical literacy, reasoning and thinking

2. aspects to consider when giving meaning
to the represented data:
1. Look behind the data
• Data sets are related to a context and gathered and presented by someone who
might have a particular agenda. It is therefore important to look behind the data.
Questions that are to be considered are: is there bias, attempts to disguise some
data, attempts to mislead with data, attempts to present the data from only one
point of view? Misuse and abuse of statistics are to be an important aspect of
pupils’ data handling experiences.
2. Look at the data
• This covers computational and representation aspects: which statistics are
meaningful to compute and what is the best way to represent the data in chart or
diagram.
3. Look between the data
• This is the comparison aspect of the analysis: looking for differences and
similarities.
4. Look beyond the data
• This is to cover the inference part of the analysis: what conclusion can be (safely)
drawn from the results.

3. Statistical literacy
• involves understanding and using the basic language and tools of
statistics
• knowing what basic statistical terms mean, understanding the use
of simple statistical symbols, and recognizing and being able to
interpret different representations of data
Statistical reasoning
• the way people reason with statistical ideas and make sense of
statistical information.
• understanding and being able to explain statistical processes, and
being able to interpret statistical results
Statistical thinking
• a higher order of thinking than statistical reasoning.
• deep understanding of the theories underlying statistical processes
and methods as well as understanding the constraints and
limitations of statistics and statistical inference.

4. Ways of discussing statistics concepts
• A conceptual perspective focuses on clarifying
what notions underpin the measures and why
these notions are important
• whereas an operational perspective focuses
on how a specific set of data is captured,
displayed and manipulated by the measures

5. Points to Consider
• What characteristics of a data set make it
easier or harder to represent using dot plots,
stem and leaf plots, histograms, and box and
whisker plots?
• Which plots are most useful to interpret the
ideas of shape, center, and spread?
• What effects do other transformations of the
data have on the shape, center, and spread?