[fruitful_sep] Like in science, sport or any other field, the GMAT requires a mastery of a specific set of theory in order to do well. Doing well means being flexible enough to apply your knowledge in different scenarios and situations. Consider sailing for example. Imagine that you take your first class one …
There is no way around it: to improve your GMAT score you must improve your reading comprehension skills. There are three parts in the verbal section of the test: sentence correction, critical reasoning, and reading comprehension. Whether you are an avid or novice reader, going through the verbal section can …
Advanced reading comprehension on the Gmat Verbal is the core skill required for a good verbal score. More than just how fast you read, how well you understand what you have just read is more important. For example, did you need to go back and re-read the first sentence? That would be …
To many, Gmat critical reasoning is the hardest section to improve, especially with little time left to prepare. Unlike quant and sentence correction, critical reasoning does not have a set of rules or syllabus you should learn. However, you can improve your performance if you are more process oriented. You should: …
The only Gmat hacks you need to know are in fact the ones that the Gmat test makers themselves mention in their Official guide books. A few are: Don’t spend too much time on any one question, if you are struggling guess and move on, read the question carefully and understand what …
A picture is worth a thousand words, or so goes the saying. That cannot be more true on the Gmat. Remember that on average, you have 2 min per Gmat Quant question, and just under 2 min per Verbal question. You do not have time to waste, and you must …
Visualization is a very powerful technique on the Gmat. In another blog, we demonstrate the value of the technique in answering data sufficiency questions. When you present the problem visually, it becomes easier to pan out the possible outcomes, recognize a pattern, and make less careless mistakes. Particularly in data …