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I am happy to announce that in the new year we will be running a series of workshops for Cork Citizen Scientists. In contrast to the small series of lectures we ran this year, the new workshops will be completely focused on supporting local citizen scientists in their efforts to answer real questions with real data.


Remember the whole Google Memo thing that happened a hundred years ago? Its central argument (as far as I could tell) was that the large male to female sex ratios we observe in Tech can be reasonably explained by small differences in the sex-specific probability distributions of innate characteristics. Thus Google’s attempts to increase diversity were silly at best, perhaps harmful or unjust, and largely due to a culture of political correctness that was oppressive to “conservative viewpoints”.


A colleague in food science recently sent me a narrative review outlining some of the challenges in their field. One of these was “extensive heterogeneity in the response to increased intake [of flavonoids]”. So-called response heterogeneity is often highlighted to justify the need for precision medicine, but there is a problem with this: the studies that are used to demonstrate response heterogeneity simply don’t.


I was recently asked to help analyze some assessment data. There were 50 people applying for seven positions. Each person’s application materials were scored by three people, randomly chosen from a larger group of raters. I was asked to help account for the fact that some raters might have a tendency to give higher or lower than average scores. If you want to play along, you can download the data here:


When using linear regression, when should you log-transform your data? Many people seem to think that any non-Gaussian, continuous variables should be transformed so that the data “look more normal.” Linear regression does in fact assume the errors are normally distributed, but it is fairly robust to violations of this assumption, and there are no such assumptions regarding the predictor variables. What is often ignored or misunderstood is the impact that variable transformations have on the linearity assumption of regression models, and on coefficient interpretation.


Recent Publications

  • Associations of linear growth and relative weight gain during early life with adult health and human capital in countries of low and middle income: findings from five birth cohort studies


  • A structural equation model of the developmental origins of blood pressure

    Details PDF


  • Western Gateway Building 4.23
  • Email for appointments