Stephen Senn. (19:12:01 UTC). De Finetti meets Popper [Data & Analytics]. https://www.slideshare.net/StephenSenn1/de-finetti-meets-popper

ReconfigBehSci. (2021, February 1). @islaut1 @richarddmorey I think of strength of inference resting on P(not E|not H) (for coronavirus case). Search determines the conditional probability (and by total probability of course prob of evidence) but it isn’t itself the evidence. So, was siding with R. against what I thought you meant ;-) [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1356216290847944706

ReconfigBehSci. (2021, February 2). @MichaelPaulEdw1 @islaut1 @ToddHorowitz3 @richarddmorey @MaartenvSmeden as I just said to @islaut1 if you want to force the logical contradiction you move away entirely from all of the interesting cases of inference from absence in everyday life, including the interesting statistical cases of, for example, null findings—So I think we now agree? [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1356530759016792064

Cousins, R. D. (2017). The Jeffreys–Lindley paradox and discovery criteria in high energy physics. Synthese, 194(2), 395–432. https://doi.org/10.1007/s11229-014-0525-z

Freedman, D. A. (n.d.). Ecological Inference and the Ecological Fallacy. 7.

Blakely, Tony, John Lynch, Koen Simons, Rebecca Bentley, and Sherri Rose. ‘Reflection on Modern Methods: When Worlds Collide—Prediction, Machine Learning and Causal Inference’. International Journal of Epidemiology 49, no. 6 (1 December 2020): 2058–64. https://doi.org/10.1093/ije/dyz132.

Perez Santangelo, A., & Solovey, G. (2020, November 9). Time to Shine: Reliable Response-Timing Using R-Shiny for Online Experiments. https://doi.org/10.31234/osf.io/nuxdg

In both cases, the answers should be clear when looking at the evidence and the mechanisms at play without an ideological bias

Data Provenance

The discipline of thinking about:

(1) where did the data arise? (2) what inferences were drawn (3) how relevant are those inferences to the present situation?

Kunin, D. (n.d.). Seeing Theory. Retrieved October 27, 2020, from http://seeingtheory.io

The Alan Turing Institute: Causal Inference, Causal Decision Making Under Uncertainty | CogX 2020. (2020, June 25). https://www.youtube.com/watch?v=JAGRHbDLvUs

Speelman, C., & McGann, M. (2020). Statements about the Pervasiveness of Behaviour Require Data about the Pervasiveness of Behaviour [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/bxzm4

Young, J.-G., Cantwell, G. T., & Newman, M. E. J. (2020). Robust Bayesian inference of network structure from unreliable data. ArXiv:2008.03334 [Physics, Stat]. http://arxiv.org/abs/2008.03334

Manski, C. F., & Molinari, F. (2020). Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem (Working Paper No. 27023; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27023

Leininger, A., & Schaub, M. (2020). Voting at the dawn of a global pandemic [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/a32r7

Méndez, P. F. (2020). Blue uncertainty: Warding off systemic risks in the Anthropocene – Lessons from COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/z2br5

Zhang, L., & Peixoto, T. P. (2020). Statistical inference of assortative community structures. ArXiv:2006.14493 [Cond-Mat, Physics:Physics, Stat]. http://arxiv.org/abs/2006.14493

Causal inference isn’t what you think it is. (n.d.). LSHTM. Retrieved June 26, 2020, from https://www.lshtm.ac.uk/newsevents/events/causal-inference-isnt-what-you-think-it

Marshall, B. D. L., & Galea, S. (2015). Formalizing the Role of Agent-Based Modeling in Causal Inference and Epidemiology. American Journal of Epidemiology, 181(2), 92–99. https://doi.org/10.1093/aje/kwu274

But these facts bear little consequence in day-to-day interactions hence their exotic status. People confuse less and fewer because using one or the other rarely changes the interaction. Calling a cashew a seed or a nut really doesn't change much.

Inference: Leaves show great variations to spread chloroplast over a large surface area to maximize light absorption. At the same time, internal leaf structure needs to optimize carbon exchange. Especially since carbon fixation is rate limiting. Therefore, there should be a relationship between leaf area and leaf structure. How strong is this relationship?

tried to discover and learn from the people form the culture

involves making inferences and knowing back round knowledge

Brady needed no knowledge of this activity. Safe to assume he told them that he likes 12.5 and that he is upset when they are inflated higher e.g. 16 psi.

Again, this is another negative inference that can easily be considered normal behavior in this situation.

Seemingly normal whether guilty or innocent.

If there have been instances of balls being inflated by referees to 16, it is plausible that Brady would instruct the guy who gives the balls to the officials to make sure they stay at 12.5.

This is another inference to the negative. If you are implicated in something with someone who works with/for you is it a natural reaction to stop communicating? Is it more natural to speak with that person? How does behavior change when the entire global media is involved?