eNeuro Keeps Up the Good Work on Estimation
Back in August Bob posted (here) about eNeuro‘s great initiative to encourage authors to use estimation. The latest eNeuro email update reports how the journal is keeping up the good work. See three paras below:
eNeuro encourages authors to add estimation statistics to their analyses when appropriate. Below, we will feature papers that did so, along with an author’s response to the query: What value was added to your analysis or perspective through the addition of estimation statistics? For more information read Estimation for Better Inference in Neuroscience.
Circuit-specific dendritic development in the piriform cortex
Laura Moreno-Velasquez, Hung Lo, Stephen Lenzi, Malte Kaehne, Jörg Breustedt, Dietmar Schmitz, Sten Rüdiger, and Friedrich W. Johenning
“It was satisfying to see how switching to estimation stats based data display increased the transparency of our data display in a reader-friendly format. By openly displaying the mean effect sizes and confidence intervals, we felt relieved from the pressure to solely rely on p-value based true/false statements about the data. Estimation statistics also makes it obvious to us and our readers where replications and further experiments are most needed in the future.” — Friedrich W. Johenning
—that’s all great to see. Below are a couple of small parts of the figures that the author refers to:
The lower means and CIs, with plausibility curves, illustrate the estimated differences between the means of the blue dots (L2B cells, whatever they are) and red dots (L2A cells). The author mentioned ‘transparency’; indeed, the pictures do make it easy to appreciate the differences, and the precision with which each was estimated.
I especially love the author’s final sentence:
Estimation statistics also makes it obvious to us and our readers where replications and further experiments are most needed in the future.
Science as a cumulative, progressive enterprise–powered by estimation. Yay!
Ho, J., Tumkaya, T., Aryal, S. et al. Moving beyond P values: data analysis with estimation graphics. Nat Methods 16, 565–566 (2019). https://doi.org/10.1038/s41592-019-0470-3