Many students and families heavily rely on the college acceptance data (GPA/test scores vs. college outcomes) of past applicants from their high school to make high-stakes decisions about their school list, selection of early decision colleges, and likely overall college outcomes. 

In this episode, we break down how past college acceptance data is reported at many high schools, and the significant limitations and shortcomings of using such data for students applying to the most selective colleges. In particular, we cover the following:

1. The Limited Predictive Power of GPA/Scores When Applying to Top Colleges
2. The Necessity of Only Comparing to Past Applicants Who Applied Under the Same Decision Plan (ED, EA, RD, etc)
3. Why Graph Averages Can Be Misleading
4. How To View Outliers and Avoid Wishful Thinking
5. Data Expiration: What HS Graduating Classes You Should Be Using
6. The Imperfections of Self-Report Data and Various Inaccuracies/Missing Information
7. The Importance of Sample Size and the Tradeoff With High Quality Comparison Cases
8. Complexities of Test Score Reporting: Test-Optional, Superscores, and Beyond
9. Limitations of GPA, Accounting for Senior Year Grades, and Why The GPA Admissions Officers See Is Often Not The One Reflected in HS Data Systems

Final Takeaways: Best Practices for Analyzing and Using High School Acceptance Data

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“The Game” is hosted by Sam Hassell and brought to you by Great Minds Advising.

Great Minds Advising’s unique, hands-on mentorship program and its deep strategic insight into the application review process have earned the company a nation-leading track record of excellence, with 100% of its students gaining admission to top-choice schools in the 2023–24 application cycle.

Its students have recently gained admission to Princeton, Columbia, Yale, Stanford, Penn, Brown, Duke, Dartmouth, Cornell, Vanderbilt, WashU, and UC-Berkeley (among many others) and are admitted to the Ivy League at a rate 14x the national average (90% when applying early).

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