In the field of Science, and Psychology understanding the data is crucial. This is because unlike many companies in media, Scientists value the true results. Whereas the media researchers interpret their data to get the results they want, rather than to get the true, accurate, and unbiased results. According to Mark Suster’s research (1) almost 75 % of all media statistics are manipulated to some extent or even made up. The other downside is that people believe statistics, and there is very little we can do about it. Almost 79% agreed with the statement “Statistics can be trusted to give an accurate description of the facts” (2).This is why we need statistics to understand the data. Otherwise we will find it very difficult to differentiate the ‘fake’ results from the accurate and reliable ones.
Unfortunately the data on its own is not always obvious enough, to conclude whether a treatment has an effect or not. This is why researchers need to back themselves up with statistics. Scientists collect data use quantitative research that produces data in form of numbers, and qualitative research which produces non numerical data. They use tools like SPSS to help them decide whether or not there is an effect. They will use software like Excel to plot graphs, histograms, and bar charts that will help them to visualise the data. Before they get any conclusion, they will need statistical knowledge to interpret and analyse their results. In case of something not going as planned, researchers would need to use their statistical knowledge to solve statistical problems (3). Therefore to someone who have never used statistics before, carrying out a research, can be a really hard time.
On the other hand people could say that you don’t need statistics to understand the data. They would back themselves up saying that researchers often make predictions about the data, but again are those reliable enough? And how accurate can those predictions possibly get, if made by someone who cannot understand statistics? “When you make a prediction, would you rather be correct or incorrect?” (4). Clearly, guessing is not science.