Throughout my career, I’ve become a bit wary of institutions that claim to be the best and specify exceptional candidates in job offers and PhD studentships…
(Shout-out to the great @Letxuga007 for the mean gif 😉)
I’d like to take this opportunity to demand the right for the less excellent or “tending towards average” to be given opportunities and have their well deserved place in Academia!
This hyperbolic language is exclusionary and will not only deter the “average” student from applying but also very smart yet humble candidates who are perhaps more realistic and indeed honest in their self assessments 🤔
Do we really want to encourage competitive environments with many top individuals (according to the current biased metrics) or top collaborative environments with a variety of individuals skilled in different ways and at different levels? I’d definitely go for the latter!
Wouldn’t it be nice to also encourage extra-ordinary career paths to enrichen homogeneous departments?
I’m obviously biased in this respect, I’m “not normal” myself and definitely “average” but I’m not a competitive person and I know I won’t reach excellence so I’m just happily striving to give my best.
I firmly believe inclusivity shouldn’t be just about ticking boxes in a form to get a certificate, and should make real differences in the way our organisations look and operate, also in terms of varied skills and knowledge.
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During my leave I’ve really enjoyed reading about the inspiring women trailblazers in statistics who paved the way for us. Here are some of my favourite quotes in chronological order. Please share yours! #WSDS
Florence Nightingale states in her essay Cassandra 👇
🖼 source: Wikimedia commons
I’m really looking forward to attending this 👇 #Nightingale2020 has been one of the few things worth celebrating this year! Her lessons on sanitation couldn’t be more relevant. #WSDS
As part of the bicentennary celebrations of the birth of the first @RoyalStatSoc woman elected fellow, at the society we’ve also organised several events throughout the year rss.org.uk/news-publicati…
Support mechanisms for students and early career researchers have become ever so important during the pandemic, yet more difficult to provide.
🖼️Another beautiful and on-point creation by @allison_horst
@allison_horst As a consequence, the power and potential of the support they receive from online communities like this one have been strengthened by the circumstances. I have personally valued them more than ever.
@allison_horst When I registered to curate this account earlier in the year I didn’t know there was going to be either a pandemic or elections. I just thought it would be a nice way to return to work after extended maternal leave, and a great way to get my confidence & stats interests back.
Tweetorial on going from regression to estimating causal effects with machine learning.
I get a lot of questions from students regarding how to think about this *conceptually*, so this is a beginner-friendly #causaltwitter high-level overview with additional references.
One thing to keep in mind is that a traditional parametric regression is estimating a conditional mean E(Y|T,X).
The bias—variance tradeoff is for that conditional mean, not the coefficients in front of T and X.
The next step to think about conceptually is that this conditional mean E(Y|T,X) can be estimated with other tools. Yes, standard parametric regression, but also machine learning tools like random forests.
It’s OK if this is big conceptual leap for you! It is for many people!
This varies *a lot* by type of role, seniority, and institution.
I’m tenured at a research-intensive institution and I am not teaching this term.
I spend a fair amount of time meeting with students and collaborators. Today, Monday, I have 5 such meetings.
There are also lots of emails and administrative tasks all the time.
Each day this week I’ll drop a tweet in this thread to add in unique things I haven’t mentioned yet to demystify the life of this particular professor.
🧵 time! I’d love to talk about the responsibilities we have as data practitioners. In this ~~information age~~ I think it’s critical we use data, ML, stats, and algorithms fairly, and with an eye toward making the world better for people.