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Isaac PugliaHost
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Isaac PugliaHost
Today, let's talk about round one versus round two versus round three and how to decide.
Of course, this gets a little bit more into the admissions consulting discussion, but I think it's so relevant to the GMAT that it's worth having the conversation here.
And so this decision of whether to apply round one or round two is top of mind for a lot of us.
And my goal today is to give you some very simple data-backed decision-making frameworks that you can use to make good choices.
The first point I want to make very relevant to that is if you have a paid admissions consultant who you trust and who knows your specific situation already, definitely defer to that person's advice over any general advice online.
But many of you can't afford an admissions consultant or you can't afford a good one, in which case definitely do not hire a mediocre one.
You're much better off just doing it on your own with freely available materials than getting someone who's just okay to help you out with the process because there's just a lot riding on this.
And so we're going to create this for you today to make sure that you have the ability to make great decisions since the decision of whether to apply round one, round two, or round three can significantly affect your odds and even some of the decisions you might be making outside of the business school decisions, such as whether to keep your current job or try to get promoted or scale down your workload and prepare to exit.
So Perhaps, obviously, this is not always an easy question to answer, round one versus round two.
I think it's difficult for a bunch of different reasons, but let's go through a few of what I think are the biggest reasons that are also worth discussing.
There are some big reasons it's hard that are kind of like obvious and not worth discussing, but there's a lot of hype around round one.
And I think it's overly emphasized how important round one is versus the other rounds.
So back to my original objective here, I want to give you some ground truth rooted in data.
Today, let's talk about round one versus round two versus round three and how to decide.
Of course, this gets a little bit more into the admissions consulting discussion, but I think it's so relevant to the GMAT that it's worth having the conversation here.
And so this decision of whether to apply round one or round two is top of mind for a lot of us.
And my goal today is to give you some very simple data-backed decision-making frameworks that you can use to make good choices.
The first point I want to make very relevant to that is if you have a paid admissions consultant who you trust and who knows your specific situation already, definitely defer to that person's advice over any general advice online.
But many of you can't afford an admissions consultant or you can't afford a good one, in which case definitely do not hire a mediocre one.
You're much better off just doing it on your own with freely available materials than getting someone who's just okay to help you out with the process because there's just a lot riding on this.
And so we're going to create this for you today to make sure that you have the ability to make great decisions since the decision of whether to apply round one, round two, or round three can significantly affect your odds and even some of the decisions you might be making outside of the business school decisions, such as whether to keep your current job or try to get promoted or scale down your workload and prepare to exit.
So Perhaps, obviously, this is not always an easy question to answer, round one versus round two.
I think it's difficult for a bunch of different reasons, but let's go through a few of what I think are the biggest reasons that are also worth discussing.
There are some big reasons it's hard that are kind of like obvious and not worth discussing, but there's a lot of hype around round one.
And I think it's overly emphasized how important round one is versus the other rounds.
So back to my original objective here, I want to give you some ground truth rooted in data.
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