@article{VasishthMertzenJaegeretal.2018,
  author    = {Vasishth, Shravan and Mertzen, Daniela and Jaeger, Lena A. and Gelman, Andrew},
  title     = {The statistical significance filter leads to overoptimistic expectations of replicability},
  series = {Journal of memory and language},
  volume    = {103},
  journal   = {Journal of memory and language},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {0749-596X},
  doi       = {10.1016/j.jml.2018.07.004},
  pages     = {151 -- 175},
  year      = {2018},
  abstract  = {It is well-known in statistics (e.g., Gelman \& Carlin, 2014) that treating a result as publishable just because the p-value is less than 0.05 leads to overoptimistic expectations of replicability. These effects get published, leading to an overconfident belief in replicability. We demonstrate the adverse consequences of this statistical significance filter by conducting seven direct replication attempts (268 participants in total) of a recent paper (Levy \& Keller, 2013). We show that the published claims are so noisy that even non-significant results are fully compatible with them. We also demonstrate the contrast between such small-sample studies and a larger-sample study; the latter generally yields a less noisy estimate but also a smaller effect magnitude, which looks less compelling but is more realistic. We reiterate several suggestions from the methodology literature for improving current practices.},
  language  = {en}
}
@article{SchlaegelLewis2016,
  author    = {Schl{\"a}gel, Ulrike E. and Lewis, Mark A.},
  title     = {Robustness of movement models: can models bridge the gap between temporal scales of data sets and behavioural processes?},
  series = {Journal of mathematical biology},
  volume    = {73},
  journal   = {Journal of mathematical biology},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {0303-6812},
  doi       = {10.1007/s00285-016-1005-5},
  pages     = {1691 -- 1726},
  year      = {2016},
  language  = {en}
}
@article{SchlaegelLewis2016,
  author    = {Schl{\"a}gel, Ulrike E. and Lewis, Mark A.},
  title     = {A framework for analyzing the robustness of movement models to variable step discretization},
  series = {Journal of mathematical biology},
  volume    = {73},
  journal   = {Journal of mathematical biology},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {0303-6812},
  doi       = {10.1007/s00285-016-0969-5},
  pages     = {815 -- 845},
  year      = {2016},
  abstract  = {When sampling animal movement paths, the frequency at which location measurements are attempted is a critical feature for data analysis. Important quantities derived from raw data, e.g. travel distance or sinuosity, can differ largely based on the temporal resolution of the data. Likewise, when movement models are fitted to data, parameter estimates have been demonstrated to vary with sampling rate. Thus, biological statements derived from such analyses can only be made with respect to the resolution of the underlying data, limiting extrapolation of results and comparison between studies. To address this problem, we investigate whether there are models that are robust against changes in temporal resolution. First, we propose a mathematically rigorous framework, in which we formally define robustness as a model property. We then use the framework for a thorough assessment of a range of basic random walk models, in which we also show how robustness relates to other probabilistic concepts. While we found robustness to be a strong condition met by few models only, we suggest a new method to extend models so as to make them robust. Our framework provides a new systematic, mathematically founded approach to the question if, and how, sampling rate of movement paths affects statistical inference.},
  language  = {en}
}