Wonder why your Ph.D./Master's application is being rejected?

Here are some insider reasons (from a committee member) that result in such "Love Letters" 👇

- A Short Thread

1) First, you should know that sometimes, an independent reviewer is required (especially in the US) to review applications even after PIs must have selected their candidates. So, even if any bias slips through the PI, the independent reviewer will pick it up.
2) Having said this, there are three major issues that application reviewers/committees often find with your application. These three components make your applications almost challenging to assess.
3) NUMBER ONE - Incomplete Applications

This occurs when you do not upload the requested documents needed to evaluate your file. Think of a candidate that did not send a GRE score when the institution clearly requires it for admission.
4) NUMBER TWO - Error laden Essays

This is particularly interesting because based on reports from this resource person, it is not particularly about how little your experience is but how you communicate it error-free or at least extensively minimized. This gives headache!
5) "Too many errors and running sentences pisses me off and I begin to wonder if this applicant really took this application seriously or just submitted his or her application as part of many mass submissions just to make up the numbers", says this resources person. Take note!!
6) NUMBER THREE - Generic recommendation letters

This often presents itself as bland and generic. This kind of letter does not attempt to share practical engagements or activities carried out by the student in collaboration or independently of an advisor.
7) Filled with many "She is intelligent, intuitive, and hard-working". Okay? so can you put this in perspective? Can you give examples of situations where the student exhibited these qualities as claimed?

Now you know! Fix this ASAP.

#BigDaddyTweets #phdchat

More from Science

@mugecevik 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.
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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.
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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).
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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/

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