Dr. Björn Walther’s Post

We need to talk about misconceptions in #statistics, namely p-values ... again. Part 5 of 5: Wasserstein, Schirm, Lazar (2019) Moving to a World Beyond "p < 0.05": There’s not much we can say here about the perils of p-values and significance testing that hasn’t been said already for decades (Ziliak and McCloskey 2008; Hubbard 2016). If you’re just arriving to the debate, here’s another sampling of what not to do.

We need to talk about misconceptions in #statistics, namely p-values ... again. Part 1 of 5: Wasserstein, Schirm, Lazar (2019) Moving to a World Beyond "p < 0.05": There’s not much we can say here about the perils of p-values and significance testing that hasn’t been said already for decades (Ziliak and McCloskey 2008; Hubbard 2016). If you’re just arriving to the debate, here’s a sampling of what not to do:

We need to talk about misconceptions in #statistics, namely p-values ... again. Part 3 of 5: Wasserstein, Schirm, Lazar (2019) Moving to a World Beyond "p < 0.05": There’s not much we can say here about the perils of p-values and significance testing that hasn’t been said already for decades (Ziliak and McCloskey 2008; Hubbard 2016). If you’re just arriving to the debate, here’s another sampling of what not to do.

We need to talk about misconceptions in #statistics, namely p-values ... again. Part 4 of 5: Wasserstein, Schirm, Lazar (2019) Moving to a World Beyond "p < 0.05": There’s not much we can say here about the perils of p-values and significance testing that hasn’t been said already for decades (Ziliak and McCloskey 2008; Hubbard 2016). If you’re just arriving to the debate, here’s another sampling of what not to do.

Very happy to share my latest paper, illustrating the applicability of inverse probability weights to mitigate the impact of mischievous and inattentive respondents on survey data https://lnkd.in/gBhwUMhu #ipw  #bias

What sample size do you need to collect to conduct a meaningful analysis? A simple answer might be "more," but collecting data costs both money and time. In this blog post, I describe how to calculate the necessary sample size to achieve statistical significance using the Chernoff Bound.

Understanding Poisson Distribution through Examples More problems on Poisson distribution are given here. Learn how to apply Poisson distribution in everyday situations. #PoissonDistribution #Probability #Statistics https://lnkd.in/gWJfhXBQ

The Prevention Pyramid To learn how to implement #economic_evaluation_techniques (#CEA and #CUA) using decision analytic models, including #decision_trees and #Markov_models in a one-to-one setting: https://lnkd.in/dqTpcvTM.

Inconvenient truths of Fisher information matrix (FIM): ① FIM can be singular (not positive definite) ② FIM can be undefined (∞) ③ FIM can be preserved under insufficiency (always preserved by sufficient statistics) Regular FIM ➡️ metric 👉https://lnkd.in/gAk5qH7

Avoiding #Bias in Observational Studies To learn how to implement #economic_evaluation_techniques (#CEA and #CUA) using decision analytic models, including #decision_trees and #Markov_models in a one-to-one setting: https://lnkd.in/dUcpjq6p.