People working at all sorts of jobs are in the business of data collection and presentation. Business consultants need to present clear pictures of past, present and future business trends which can be illustrated with a host of props such as pie charts, graphs etc. These show things like the rate of decline of customers after marketing their revolutionary motorbike ashtray or a host of other things.

Businesses use computers to gather information on all their customers and suppliers and this information is compiled into meaningful reports and analyses. It's not all trendy suits and liquid lunches with the advertising agency for marketing people either as they spend a good deal of their time analysing the market to see if customers will want to buy their product. Other examples of jobs where data is important are: politics (though it's more important to understand how to manipulate data to your advantage here!), and all forms of scientific research though noteably trials for new medicines etc.

So, it's important that you have a grasp of the basics of data collection and presentation if you want to climb the career ladder already starting half-way up.

There are two main types of data:

QUALITATIVE - The data cannot be measured using numbers - e.g. Favourite TV programs, Hair colour, peoples' opinions/feelings on any subject, personality etc.

QUANTITATIVE - The data can be measured using numbers - e.g. Shoe sizes, age, No. of hours spent watching TV on a Wednesday etc. Think quantitative = quantity to remind you of which is which.

Quantitative data can exist in 2 forms:

Discrete - Only has an exact value e.g. shoe sizes 7, 7.5, 8, 8.5, 9 etc.

Continuous - Cannot be measured exactly as the accuracy of the data depends on what you measure it with e.g. size of your feet can be 18.5cm when measured in the shoe shop but if you use a laser foot scanner (if one exists) then a more accurate size could be 18.645cm. "Visitors" from another planet may be able to measure your foot to be 18.64487747478! Continous data is presented in groups e.g. size of feet can be greater than 18cm and less than or equal to 20cm, or greater than 20cm and less than or equal to 22cm etc.

Note that you can calculate the mean of continuous data by adding the mid-points of all the groups and dividing by the number of mid-points. Just to make things awkward, the groups are given a special name: Class Intervals.

Large samples of data can be difficult for people to understand with zillions of numbers in tables. So, there are several ways to spruce up your data to make it easy to interpret.

Bar Chart - use with discrete data
If you are given discrete data you can put it into a bar chart as in the example below. The height of the bars in the chart represent the frequency (no. of times something was recorded e.g. no. of GCSEs passed by students).

Histogram - use with grouped and continous data
A histogram looks very similar to a bar chart, but it is actually very different.
Instead of the height of the bars representing the frequency it is actually the frequency density or area of the bars. The frequency density is always on the vertical axis (note that as most questions expect to to calculate frequency densities, it's unlikely that you'll see any markers or numbers on this axis).

There are no gaps between the bars (indicating that the data is continuous) in the histogram and the width of them can vary (depending on the sizes of the class intervals).

Frequency density = Frequency / Width of Bar.

Line Graph - used for grouped discrete or grouped continous data
Points are first plotted on a graph and then joined up to make a line (straight or curved). Graphs like this with frequency on the y-axis plotted against your data on the x-axis are called Frequency Polygons

and graphs with cumulative frequency on the y-axis plotted against your data on the x-axis are called Cumulative Frequency Diagrams.

Pie Chart - use with discrete data
To show how values are distributed you can use a pie chart. The size of the areas shaded on the pie chart are calculated using portions of 360^o.