TheFork

Yeah, I'm trying to draft an email to reject some people. I feel fortunate but part of it sucks because you don't want to burn bridges.
If you listen to Adele's Someone Like You, and change "you" to "your program" and "someone" to "another program" it's actually a great start.

After the meeting, send a follow-up email confirming what you two spoke about. That way you have it in writing!

This is what I know thus far (attached).

Just got an email from UNC: Charlotte, their interview day is February 23rd. On my way down, I'll be sure to stop at NC State, roll my eyes, then drive into the night.

Hello all, 
To those that applied to CSU, my POI emailed me that the committee is gathering this week and has just started reviewing applications. 
Best of luck to everyone! 

Congratulations to the person who got accepted at Michigan State!  

Hello everyone,
I've seen quite a bit of chatter around the timing of results, and the answers are obscure. I wanted to provide my services, and give you all some extra data. The beauty of grad cafe is that it is one of the most (if not the most) centralized data source for graduate students. Some data examples: What schools are most popular, what degrees, applicant metrics, dates of results, etc. Yet, I've yet to find someone that uses this information to help those applying.
 
Until now...
 
I used R (for those unfamiliar) to scrape the grad cafe results data related to psychology. I returned about 35000 records. There were a lot of un-tidy text strings (e.g., PhD and Ph.D.), especially in relation to program-title and institution; however, that is a project for another day. Data regarding dates and decisions did seem clean enough for me to analyze it and turn it into something nice for you all.
 
So what is it....?
Here is a PDF based on the results of all Psychology students (over 35000) since the origin of grad cafe. The first 3 tables show the 10 most common dates (by count) for getting an interview, getting accepted, and getting rejected ? .  I would do relative frequency and should've...maybe tomorrow.
Then a longitudinal line graph (which shows these decisions throughout the year)
Finally, one that focuses more on the "critical-period" when most decisions are made.
 
Conclusion
This was a piecemeal job that I should be done in R Markdown. I'll link my code at the end. Similar methods could be used to look at the "most popular" programs, probability of acceptance by degree type or program type, average GRE score for accepted candidates, and the list goes on. If anyone wants a csv of the dataset I used, please feel free to message me. I also welcome critiques and suggestions.  I wish you all the best.
 
R code here