  The increasing ease of data capture and storage has led to a corresponding
increase in the choice of data, the type of analysis performed on that data,
and the complexity of the analysis performed. The main contribution of this
paper is to show that the subjective choice of data and analysis methodology
substantially impacts the identification of factors and outcomes of
observational studies. This subjective variability of inference is at the heart
of recent discussions around irreproducibility in scientific research. To
demonstrate this subjective variability, data is taken from an existing study,
where interest centres on understanding the factors associated with a young
adult's propensity to fall into the category of `not in employment, education
or training' (NEET). A fully probabilistic analysis is performed, set in a
Bayesian framework and implemented using Reversible Jump Markov chain Monte
Carlo (RJMCMC). The results show that different techniques lead to different
inference but that models consisting of different factors often have the same
predictive performance, whether the analysis is frequentist or Bayesian, making
inference problematic. We demonstrate how the use of prior distributions in
Bayesian techniques can be used to as a tool for assessing a factor's
importance.
