Sampling, the data-handling cycle and data presentation

Learning objectives

Distinguish a population from a sample, and discrete from continuous data, and explain why a sample must be representative if conclusions are to be generalised

Describe simple random, convenience, systematic, quota and stratified sampling, and evaluate the effectiveness and the likely bias of each in a given situation

Assess the reliability of a data source, identifying sources of bias such as self-selection, leading questions or non-response

Organise raw data into a grouped frequency table with sensible equal class widths, and draw a histogram whose bars touch to show that the data are continuous

Draw a cumulative frequency graph and use it to estimate the median, the quartiles, a percentile, the range and the interquartile range

Draw and interpret a box and whisker diagram, identify outliers as values more than 1.5 × IQR beyond the nearer quartile, and compare two distributions by their box plots