Jeremy Weber: Statistics for Public Policy | Tom Nelson Pod

Jeremy Weber teaches and researches the policy and economics of environmental and energy issues. His work applies rigorous statistical analysis and draws from extensive policy experience, having worked as a research economist at a Federal statistical agency (the Economic Research Service) and as a chief economist at the White House (the Council of Economic Advisers).
00:00 Introduction to Jeremy Weber and His Background
00:45 The Big Picture: Statistics for Public Policy
01:04 Understanding Data's Role in Policy Making
02:47 The Map Analogy: Navigating Data and Policy
07:37 Real-world Policy Implications and Challenges
11:51 Magnitude Matters: Interpreting Statistical Significance
24:24 Debating Climate Change Projections and Policy
43:42 Exploring Other Environmental and Policy Issues
47:28 The Role of Experts in Public Discourse
54:53 Closing Thoughts and the Importance of Questioning
How to Avoid Lying With Statistics (with Jeremy Weber) 3/4/24: t.co/TVLVK8oC3E
Making Statistics Understandable For Public Policy with Dr. Jeremy Weber (Chasing Leviathan): t.co/ugpAZCxQCl
Statistics for Public Policy: A Practical Guide to Being Mostly Right (or at Least Respectably Wrong): www.amazon.com/dp/0226830756
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[@benjones1717]

Long story short don't go on holiday with Jeremy Weber.

[@JPS-hd8qz]

First: greetings from Belgium. Second: there is always something new to learn here. And, Tom, get Randall Carlson on your podcast.

[@padraigadhastair4783]

Another brilliant interview, thanks Tom. Thanks Jeremy for you excellent presentation.

[@RiCsoundbox]

Cool! Glad to see him on your podcast! 😎😎😎

[@user-xu2pn3de9w]

Thank you Jeremy for this recognisable and clear presentation and thank you Tom for giving the floor. Even though any science having regard the use or abuse of statistical analysis in any environment like public policy is most interesting due to the impact on personal freedom and life, the utmost important point having regard statistics remains the one that my professor of statistics taught me 45 years ago in his first lecture: “Tell me what you want to know and I will define the corresponding statistics for you”. Anyone who uses statistical information must be aware that in many cases a statistical information can be achieved showing the opposite.

[@egoncorneliscallery9535]

Nice to hear a clear thinker trying to make sense, weighing up variables, looking at things from various perspectives and using statistics to get to positive outcomes, something that has been virtually eliminated from official state bodies.

[@dongaetano3687]

This is sounding very interesting Tom. I'll finish later.

[@rikardengblom6448]

Thanks!

[@woodchipgardens9084]

4 seasons and hours of Daylight are unbreakable unless you can prove a change in Angular momentum.

[@tim2muntu954]

Hume stands un-refuted since the 18th Century when he expressed the maxim that an ought cannot be derived from an is. (cannot derive the imperative from the indicative).

[@BertWald-wp9pz]

We are seeing an increase in use of science and data to justify policy and though it is important to consider evidence it clearly does not eliminate mistakes. The problem is that when mistakes are made there seems far less interest in reviewing the data afterwards. Politicians and pressure groups cherry pick when making policy and cherry pick to cover up after mistakes. I have pondered this problem and feel the only answer is to have a red and blue team approach.
If the red team proposes policy the blue team reviews it and there should be no interference or limitation on the scope of enquiry.
On data production, I wonder what would happen if some country, let us say it was Sweden (as they defied the Covid narrative) , starts to fund an institution to research areas of climate currently considered no go areas. In time, if their predictions proved more valid politicians in other countries, realising that they risked being proven wrong might think again. It would also stop academics feeling their research can only have certain outcomes and not others or they will lose employment. There would be a place for such people to go. The issue is the closed shop. Really data loses credibility when there is only one acceptable conclusion.
‘Keep asking the questions’, is a good message.

[@egoncorneliscallery9535]

Anything to do with GDP can only be assessed short term, say 5 years. Error margins after that are simply too big w high uncertainty. That goes for climate change negatives as well as positives. Climate economists should not use it for long term projections. Simply because you presume a linearity that cannot be confidently stated even IF you look at historical figures. In fact it is the big mistake Bjorn Lomborg et al make, just like the climate alarmists. In an economic flux GDP goes haywire. And the austrian school of economics with their equalibrium model are equally mistaken as are the Keynesians. They need to be rejected a priori.
This whole GDP business is handy for statisticians looking a few years ahead.

[@Nuts-Bolts]

If politicians aren't accountable for their skill and ability to choose the right experts to listen to, then it would be cheaper to sack them all and just employ school leavers on internships to lead the country, based on anything they they hear from ‘expert talking-heads’ who sound good to them. There would be no difference.

[@kenmolloy1645]

It’s called confirmation bias.

[@jamesgreig5168]

Wow!! Jeremy is so frustrating to listen to.
Tom asked a great question about warmth increasing many factors for human and the response was absolute rubbish!!!
I'll keep watching but thus guy is impossible to listen to. And I'm a statistician
!!!!