![]() The same applies to our sample size: we are often unable to analyse a “whole population” sample and so have to generalize from our observations on a limited size sample to the whole population. The basic problem of ‘significance’ is simple: it is simply unpractical to repeat an experiment an infinite number of times to make sure that what we observe is “universal”. ![]() In our paper, published three days before the ASA’s statement, we argued that the most commonly used tool in the social sciences for calculating significance – the p-value – is misused, misunderstood and, most importantly, doesn’t tell us what we want to know. In particular, the deluge of data has made it crucial that we can work out whether studies are ‘significant’. Because much as datafication has created huge social opportunities, it has also brought to the fore many problems and limitations with current statistical practices. This reflects a growing concern in academic circles that whilst a lot of attention is paid to the huge impact of big data and algorithmic decision-making, there is considerably less focus on the crucial role played by statistics in enabling effective analysis of big data sets, and making sense of the complex relationships contained within them. In an unprecedented move, the American Statistical Association recently released a statement (March 7 2016) warning against how p-values are currently used. ![]() “Significant”: an illustration of selective reporting and ![]()
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