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  Compared to random sampling, that is much less exact (since only a portion of the population is considered, on this case, that portion which has
  a presence on Prolific), however it is very value-efficient compared to the opposite strategies.
  
  We selected a cluster-randomized sampling strategy (Gravetter
  and Forzano, 2018): quite a few potential respondents have been selected at random from the Prolific
  population. Baltes and Ralph (2020) not too long ago recommended that the majority studies in software program
  engineering analysis use non-likelihood sampling, such as purposive or
  comfort sampling, and lower than 10% use random sampling.
  The selection of sampling strategy is essential for sample analysis.
  
  The ultimate sample included 483 complete responses, representing the biggest sample for a personality analysis study in software engineering (Cruz et al, 2015).
  Along with data on character traits, we also collected demographic data.
  The curiosity in Bayesian statistics is rising within the empirical software program engineering research neighborhood (Furia, 2017; Furia et al, 2019, 2021).
  One motive for this is an growing consciousness of the limitations
  of ‘traditional’ frequentist statistics, which relies on p-values and
  significance testing of null-hypotheses (Cohen, 1994; Baker, 2016).
  By convention, p-values are thought-about as a measure of evidence in favor (or towards) of a
  null speculation.
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