Category Archives: Student t distribution

“Bayesian replication analysis” (by John Kruschke)

“… the ability to express [hypotheses] as distributions over parameters …” Bayesian estimation supersedes the t-test: (Also by Professor Kruschke.)

Posted in American Statistical Association, Bayesian, John Kruschke, model comparison, rationality, rhetorical statistics, statistical models, statistics, Student t distribution | Leave a comment

Dikran Marsupial’s excellent bit on hypothesis testing applied to climate, or how it should be applied, if at all

Frankly, I wish some geophysicists and climate scientists wrote more as if they thoroughly understood this, let alone deniers to try to discredit climate disruption. See “What does statistically significant actually mean?”. Of course, while statistical power of a test … Continue reading

Posted in Anthropocene, anti-science, Bayesian, climate change, climate data, climate disruption, D. K. Marsupial, Frequentist, global warming, hiatus, Hyper Anthropocene, ignorance, John Kruschke, regime shifts, statistics, Student t distribution | Leave a comment

“Catching long tail distribution” (Ted Dunning)

One of the best presentations on what can happen if someone takes a naive approach to network data. It also highlights what is, to my mind, the greatly underappreciated t-distribution, which is typically only used in connection with frequentist Student … Continue reading

Posted in Cauchy distribution, complex systems, data science, Lévy flights, leptokurtic, mathematics, maths, networks, physics, population biology, population dynamics, regime shifts, sampling, statistics, Student t distribution, time series | Leave a comment

p-values and hypothesis tests: the Bayesian(s) rule

The American Statistical Association of which I am a longtime member issued an important statement today which will hopefully move statistical practice in engineering and especially in the sciences away from the misleading practice of using p-values and hypothesis tests. … Continue reading

Posted in approximate Bayesian computation, arXiv, Bayes, Bayesian, Bayesian inversion, bollocks, Christian Robert, climate, complex systems, data science, Frequentist, information theoretic statistics, likelihood-free, Markov Chain Monte Carlo, MCMC, Monte Carlo Statistical Methods, population biology, rationality, reasonableness, science, scientific publishing, statistical dependence, statistics, stochastics, Student t distribution | Leave a comment

Hansen et al.

Tamino weighs in on the Hyper-Anthropocene paper by Hansen, Sato, et al, references in my postings here as https://667-per-cm.net/2015/07/23/welcome-to-the-hyper-anthropocene/ and https://667-per-cm.net/2015/07/27/professor-james-hansen-responds-and-explains/ Update, 18th October 2015 To quote Eli Rabett of Rabett Run, EliRabett said… Evidently today the editor has decided … Continue reading

Posted in adaptation, Antarctica, Anthropocene, Arctic, astrophysics, bifurcations, bridge to nowhere, carbon dioxide, carbon dioxide capture, Cauchy distribution, chance, civilization, climate, climate change, climate disruption, climate zombies, COP21, denial, differential equations, dynamical systems, ecology, economics, environment, ethics, floods, forecasting, games of chance, geophysics, global warming, Hyper Anthropocene, IPCC, James Hansen, mathematics, maths, meteorology, nor'easters, oceanography, physics, politics, probability, rationality, reasonableness, science, sea level rise, statistics, Student t distribution, Tamino, temporal myopia, the right to know, transparency, UNFCCC, zero carbon | Leave a comment

“Cauchy Distribution: Evil or Angel?” (from Xian)

Cauchy Distribution: Evil or Angel?. From Professor Christian Robert.

Posted in arXiv, Bayes, Bayesian, Cauchy distribution, information theoretic statistics, mathematics, maths, optimization, probabilistic programming, probability, rationality, reasonableness, statistics, stochastic algorithms, stochastics, Student t distribution | Leave a comment