Comparing Data Sets

Data sets are groups of data gathered to help in finding an answer or hypothesis.
Identifying the type of data is critical. Data can be quantitative (numerical) or qualitative (descriptive)
Quantitative data can be discrete (countable) or continuous (measurable).
Qualitative data may be nominal (categories) or ordinal (ordered categories).

Look for patterns, trends, or peculiarities in the data that could be of importance.
When comparing data sets, consider the mean, median and mode to describe central values.
Understand the range, which shows the spread of the values in a data set.
The interquartile range can be used to describe dispersion in the data. It measures the spread of the middle 50% of values in the ordered data set.
In comparative analysis, data sets can also be evaluated by their variance and standard deviation - measures of how spread out the numbers are from the mean.

Create bar charts, histograms or pie charts, which can visually compare data sets in a meaningful way.
A box-and-whisker plot shows the median, quartiles, and possible outliers in the data set, which aids in comparison.
A scatter plot may reveal a correlation or trend between two data sets.

Data sets are not always fully representative or entirely unbiased, which should be remembered when drawing conclusions.
Identify outliers, as they can heavily influence mean values and may not be typical of the data as a whole.
Remember, correlation between data sets does not necessarily imply causation. Many other factors could influence the results.
Data manipulation, such as standardising, can eliminate unit differences and make data sets of different scales comparable.