is an excellent scientist and a responsible professional. She likely read the paper more carefully than most. She grasped some of its strengths and weaknesses that are not apparent from a cursory glance. Below, I will mention a few points some may have missed.
1/

The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
2/
The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/
The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/
The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/
It is challenging to estimate the effect of interventions in the absence of a counterfactual. This difficulty is compounded by likely confounders, such as climate (not mentioned in the paper). Territories that implemented similar interventions might share comparable climate.
6/
This is a sophisticated piece of work, with both strengths and weaknesses. Though, to me, its primary value is the proposed methodological framework rather than its estimates of the efficacy of individual interventions, which efficacy remain debatable.
7/
There is room for scientific discussion about how solid the estimates presented in the paper may be. Though, accusing colleagues expressing reservations about the robustness of some of the findings of 'spreading disinformation' feels inadequate, to say the least.
8/
This paper has generated endless conflict. One intriguing feature is that all the spats are around 'educational settings'. Interestingly, the paper also claims that T&T and isolation of cases, among other measures, are completely ineffective, yet no one seems to care ... 🤔
9/

More from Science

It was great to talk about reproducible workflows for @riotscienceclub @riotscience_wlv. You can watch the recording below, but if you don't want to listen to me talk for 40 minutes, I thought I would summarise my talk in a thread:


My inspiration was making open science accessible. I wanted to outline the mistakes I've made along the way so people would feel empowered to give it a go. Increased accountability is seen as a barrier to adopting open science practices as an ECR

It also comes across as all or nothing. You are either fully open science or your research won't get anywhere. However, that can be quite intimidating, so I wanted to emphasise this incremental approach to adapting your workflow

There are two sides to why you should work towards reproducibility. The first is communal. It's going to help the field if you or someone else can reproduce your whole pipeline.


There is also the selfish element of it's just going to help you do your work. If you can't remember what your work means after a lunch break, you're not going to remember months or years down the line

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A THREAD ON @SarangSood

Decoded his way of analysis/logics for everyone to easily understand.

Have covered:
1. Analysis of volatility, how to foresee/signs.
2. Workbook
3. When to sell options
4. Diff category of days
5. How movement of option prices tell us what will happen

1. Keeps following volatility super closely.

Makes 7-8 different strategies to give him a sense of what's going on.

Whichever gives highest profit he trades in.


2. Theta falls when market moves.
Falls where market is headed towards not on our original position.


3. If you're an options seller then sell only when volatility is dropping, there is a high probability of you making the right trade and getting profit as a result

He believes in a market operator, if market mover sells volatility Sarang Sir joins him.


4. Theta decay vs Fall in vega

Sell when Vega is falling rather than for theta decay. You won't be trapped and higher probability of making profit.
1/“What would need to be true for you to….X”

Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?

A thread, co-written by @deanmbrody:


2/ First, “X” could be lots of things. Examples: What would need to be true for you to

- “Feel it's in our best interest for me to be CMO"
- “Feel that we’re in a good place as a company”
- “Feel that we’re on the same page”
- “Feel that we both got what we wanted from this deal

3/ Normally, we aren’t that direct. Example from startup/VC land:

Founders leave VC meetings thinking that every VC will invest, but they rarely do.

Worse over, the founders don’t know what they need to do in order to be fundable.

4/ So why should you ask the magic Q?

To get clarity.

You want to know where you stand, and what it takes to get what you want in a way that also gets them what they want.

It also holds them (mentally) accountable once the thing they need becomes true.

5/ Staying in the context of soliciting investors, the question is “what would need to be true for you to want to invest (or partner with us on this journey, etc)?”

Multiple responses to this question are likely to deliver a positive result.