Having a strong statistical background provides persuasive support for your research being valid. From academics to the general public, statistics act as justifications for an argument because they provide evidence for the trends you have found. Presenting a piece of academic research with a P < 0.01 shows support for the hypothesis because there is only a small chance of any error obscuring a significant result. Using such precision also enables psychology to compete with the hard sciences- such as physics, chemistry and medicine, where such accuracy could determine an individual’s survival. It ensures people take psychological findings more seriously, so they can be used alongside other practises in order to fully resolve problems in the best way possible.
However, basing the validity of an argument on statistics alone can result in a limited understanding of the greater picture. Not every problem is just the sum of its parts. Statistics can be manipulated in order to blind people from the truth. For example in 2009 the L’oeal Elvive advertisement featuring Cheryl Cole indicated 73% of women agreed their hair felt less weak, limp and lifeless after using their product. With 356 people taking part in the study this statistic appears to support the claim the product works; (http://www.youtube.com/watch?v=y3EEIcPkcO4.) However it also means that 27% did not agree nor like the shampoo. This equates to 97 people- which is almost a third of the sample population. By looking at statistics from a different angle, varying interpretations can be formed. As seen in this example, when one statistic is used on its own as a tool of persuasion, the numbers are presented in a very appealing way. Therefore the greater a statistical background and the more analysis that takes place, the more precise and specific an understanding we gain.
In order for paradigm shifts to occur within a certain field and knowledge to progress, research must be disproved. To disprove something, definite facts and an understanding as to how these conclusions have been made must be presented. This enables future researchers to replicate the findings to check the reliability and validity of such hypothesis and enable a universal understanding of scientific believes. The most direct and clear method for doing this is not by looking at the story behind the data and second guessing why the results have turned out the way they have- resulting in potential correlations, but by finding a cause and effect relationship. This involves data being collected and statistical analogies to be done.
A strong statistical background automatically leads to greater precision, reliability and validity of any findings. If any science lacks such qualities then terrible misjudgements could be made which could have detrimental effects.
sarric 