Statistics is important when it comes to understanding data… but is it essential? Is it all that is needed in order to fully understand the extent of the data been given, or are there other things that outweigh the importance of the statistical values? When data is collected it is automatically assumed that an output will be generated in order to see whether the ‘P’ value supports the hypothesis or the null hypothesis, but should the background reading into the data be more important, should understanding why the data has been generated be more important than the actual P value?
Statistics makes it simple to understand the bare face of data, to understand whether or not the experiment has been successful. Statistics tell a story in simple terms, this is through tables and graphs. The visible data makes it easy to determine what they actually mean, by looking at a produced graph, things like correlation can be easily spotted. This can then make it easy to see if one variable has caused or affected another variable, or whether the two are simply unconnected.
Even though statistics makes it easy to determine data and to visually compare elements, I do not see it to be the absolute essential to completely understanding data. I believe other elements are also important, such as reading into why the data may have been produced, or whether there has been other extraneous variables which has effected the data output which may have not previously been thought about, also whether changing the way the experiment has been carried out could change the data in order for it to support the hypothesis previously set.
To conclude I believe statistics to be partially important when it comes to understanding results, but I do not see it as the sole compartment when it comes to being data literate.