The Most Common Words in The Harvard Crimson’s Successful Application Essays

The Ivy Institute | College Admissions Research | November 2026

Abstract

Across 88 distinct essays published in nine annual editions, the most frequent words were the (2,579), I (2,221), to (1,670), my (1,588) and and (1,522). Every essay used each of the five. After applying a fixed English stopword list, like led with 128 occurrences, while time appeared in more essays. The top five unfiltered words kept their order whenever one edition was removed. The filtered ranking was more sensitive to the editions included. Word frequency and distribution across essays answer different questions about this collection.

Introduction

An applicant who asks which words appear most often is really asking two questions. Which words occupy the most space on the page? Which ones recur across different essays? Repetition by one writer can answer the first question while having little bearing on the second. This study measures both, retaining common grammatical words so that the first-person point of view remains visible.

Pennebaker and colleagues used function words in application essays to study subsequent academic performance [3]. Their outcome data made that question testable. The Crimson collection supplies published examples and editorial commentary. It can therefore answer a narrower question about the vocabulary of a curated public genre. Alvero and colleagues also showed that essay language varies with household income and test scores in a much larger application dataset [4]. The composition of this published sample matters to any interpretation.

Publication history and study population

The print anthology 50 Successful Harvard Application Essays began in 1999. Subsequent editions appeared in 2005, 2010, 2014, 2017 and 2024. The separately packaged, sponsored online series can be traced through nine consecutive undergraduate editions, from 2018 through 2026. Its 2018 edition is independently identified in the Crimson’s own sponsorship materials [1–2]. The two formats overlap in content. For example, the 2026 journalism essay published under the heading George begins with the same newsroom assignment and opening passage as the sixth-edition publisher excerpt labeled Neb F. Publication year therefore cannot stand in for application year or establish a writer’s identity.

Materials and methods

The study recovered the ten displayed essay bodies from each of nine annual editions. For 2018–2024 it used snapshots of the Crimson page captured in the publication year. The 2025 source was the publisher’s ten-slide feature and the 2026 source its embedded ten-page presentation [2]. Contemporary snapshots matter. The 2018 and 2020 captures retain Marshall and Caden respectively. The September 2022 capture includes Michelle G.’s account of catching butterflies with a grandfather. A later version of the same edition displays Sean’s different butterfly essay, which also appears in the original 2023 edition. The publication-year version defines the 2022 observations here.

The 90 placements yielded 88 distinct texts. On sets of consecutive five-word sequences, the punk-music essays labeled Ariel (2018) and Sophia (2022) had a Jaccard similarity of 0.903. Tony’s 2022 and 2023 essays had a similarity of 1.000. No other pair exceeded 0.02. The earliest appearance of each text was retained. Two 2025 placements both headed Claire describe different experiences and were retained separately. All identifiers in this study denote published texts.

Analysis retained student prose, including dialogue, quoted language and non-English words. The extraction removed page headings, pull quotes displayed a second time, reviews, identified standalone titles and an editorial introduction. A dictionary-style opening that functions as part of the essay was retained. Tokenization normalized Unicode (NFKC), case and curly apostrophes. Runs of letters counted as words. Internal apostrophes stayed within a token, hyphens separated words, and digits were omitted. I’m and I, like learn and learned, are separate forms. These counts can differ from an application platform’s word count.

For each word, the analysis counted occurrences, occurrences per 1,000 tokens and essays containing it. The filtered comparison used scikit-learn’s English stopword list plus a fixed set of common contractions. It preserves words such as like and just. Counts are descriptive for this corpus. Ties are displayed alphabetically.

Table 1. Composition of the vocabulary corpus
EditionPlacementsDistinct texts¹Word tokens
201810106,299
201910105,752
202010106,287
202110106,111
20221095,354
20231095,780
202410106,205
202510106,629
202610106,492
Total908854,909

¹ Repeated texts are allocated to their earliest edition. Edition labels refer to publication. The 88 texts ranged from 214 to 914 tokens, with a median of 642.5. They contained 8,700 different word forms.

Results

Figure 1. The ten most frequent word forms

Occurrences in 54,909 words

the2,579
I2,221
to1,670
my1,588
and1,522
of1,469
a1,242
in895
that661
was581

Bars share a zero baseline. Common articles, pronouns, conjunctions and prepositions are included.

The five leading words accounted for 17.45% of all tokens. I and my alone accounted for 6.94%. Each appeared in all 88 essays, including essays organized around another person. This result concerns literal word forms. Counting additional first-person forms or expanding contractions would answer a broader question.

Table 2. Vocabulary after stopword filtering
WordUsesPer 1,000Essays / 88
like1282.3351
time1252.2855
life1051.9154
school1041.8952
world911.6642
just901.6452
years881.6047
day871.5849
know871.5846
people821.4936
new791.4448
family771.4032
love741.3539
did711.2936
home691.2637

Discussion

Frequency and reach diverged even near the top. Like appeared three more times than time, yet time occurred in four more essays. Family had 77 occurrences in 32 essays. Home occurred fewer times (69) but in more essays (37). A ranking by total occurrences privileges the words writers return to. Essay coverage counts each text just once.

The contrast is clearest for ye. The tokenizer splits the hyphenated family term Ye-Ye into two tokens. The resulting word form occurred 50 times across only two essays, whereas learned occurred exactly 50 times across 35. The same rank represents two different facts about the corpus. An essay-level count is essential before interpreting an unfamiliar ranked form.

Even common filtered words can perform several jobs. Like may introduce a comparison, report a preference or occur in speech. Time may mark a single occasion, elapsed years or an opportunity. The counting procedure makes no distinction among these uses. Words referring explicitly to familiar application virtues were relatively sparse: leadership occurred six times in five essays and passion 17 times in 14. The count for the literal form Harvard was eight, distributed over two essays. A third used the possessive Harvard’s, a separate token under the stated rule. These are exact-form counts. They say little about how often a writer led a group or cared deeply about an activity.

Sensitivity and limits

Restoring both repeat placements increased the token total to 56,025 and retained the order of the ten leading unfiltered words. In nine leave-one-edition-out analyses, the five most frequent unfiltered forms always kept their order. Among filtered words, time overtook like when 2019, 2020 or 2024 was omitted. A difference of three occurrences is too small to support a durable claim about which of those words characterizes these essays most strongly.

Longer texts contribute more to token totals. The source mixes different prompts, periods and publication formats. A comparison against unpublished admitted essays, unsuccessful essays or ordinary student prose would require additional corpora collected on comparable terms. Within this sample, the most dependable lexical finding is the breadth of first-person reference. The ranking of the remaining vocabulary depends on the question and the filter.

Sources and disclosures

  1. Staff of The Harvard Crimson. 50 Successful Harvard Application Essays. St. Martin’s Griffin. Edition records: 1999, 2005, 2010, 2014, 2017, 2024.
  2. The Crimson Brand Studio and Crimson Business. Undergraduate successful-essay features, edition-specific sources: 2018 · 2019 · 2020 · 2021 · 2022 · 2023 · 2024 · 2025 · 2026. 2026 Crimson host · later 2022 edition page · Crimson sponsorship prospectus.
  3. Pennebaker, J. W., Chung, C. K., Frazee, J., Lavergne, G. M., & Beaver, D. I. (2014). When small words foretell academic success. PLOS ONE, 9, e115844. Publication record.
  4. Alvero, A. J., Giebel, S., Gebre-Medhin, B., Antonio, A. L., Stevens, M. L., & Domingue, B. W. (2021). Essay content and style are strongly related to household income and SAT scores. Science Advances, 7, eabi9031. Publication record.

Study disclosure. Prepared for The Ivy Institute, which contributed professional reviews to the Crimson series, including in 2024 and 2025. Sponsor reviews, applicant profiles and advertising were excluded from the evidence.

Data availability. The accompanying research supplement includes the source register, edition and duplicate identifiers, essay-level codes, full word-frequency table, feature definitions and reproduction scripts. Source links above lead to the original publications or their archived pages. All counts describe the specified editions as reconstructed on November 8, 2026.

Essay register by edition

Open an edition to check its ten placements against the source. The counts above give repeated texts a single observation.

2018 edition
  1. Bobby — 659 tokens.
  2. Phillip — 641 tokens.
  3. Jessica — 590 tokens.
  4. Jeremy — 786 tokens.
  5. Emily — 658 tokens.
  6. Marshall — 522 tokens.
  7. Chad — 644 tokens.
  8. Ariel — 640 tokens.
  9. Margot — 510 tokens.
  10. Janice — 649 tokens.

Read the 2018 source

2019 edition
  1. Sandra — 814 tokens.
  2. Laura — 657 tokens.
  3. Kevin — 633 tokens.
  4. Samantha — 573 tokens.
  5. Julia — 621 tokens.
  6. Marina — 617 tokens.
  7. Valerie — 463 tokens.
  8. Reginald — 659 tokens.
  9. Christopher — 501 tokens.
  10. Aiden — 214 tokens.

Read the 2019 source

2020 edition
  1. Sarah — 779 tokens.
  2. Josh — 721 tokens.
  3. Danielle — 488 tokens.
  4. Winnie — 580 tokens.
  5. Lucien — 464 tokens.
  6. Chaffee — 592 tokens.
  7. Lessard — 746 tokens.
  8. John — 914 tokens.
  9. Octav — 503 tokens.
  10. Caden — 500 tokens.

Read the 2020 source

2021 edition
  1. Nicolas — 649 tokens.
  2. Nikolas — 573 tokens.
  3. Abigail — 600 tokens.
  4. Justine — 659 tokens.
  5. Carrie — 568 tokens.
  6. Jiafeng — 635 tokens.
  7. Ella — 652 tokens.
  8. Elizabeth — 640 tokens.
  9. Alex — 552 tokens.
  10. Anthony — 583 tokens.

Read the 2021 source

2022 edition
  1. Taras — 522 tokens.
  2. Yukta — 650 tokens.
  3. Eda — 635 tokens.
  4. Lisa — 502 tokens.
  5. Michelle C. — 505 tokens.
  6. Tony — 474 tokens.
  7. Yueming — 805 tokens.
  8. Charles — 505 tokens.
  9. Michelle G. — 756 tokens.
  10. Sophia — 642 tokens. Repeat of 2018-08.

Read the 2022 source

2023 edition
  1. Marina — 643 tokens.
  2. Simar — 643 tokens.
  3. Una — 655 tokens.
  4. Georgina — 646 tokens.
  5. Abby — 658 tokens.
  6. Amy — 653 tokens.
  7. Samantha — 659 tokens.
  8. Connor — 583 tokens.
  9. Tony — 474 tokens. Repeat of 2022-06.
  10. Sean — 640 tokens.

Read the 2023 source

2024 edition
  1. Sarika — 625 tokens.
  2. Francisco — 642 tokens.
  3. Billy — 604 tokens.
  4. Lauren — 650 tokens.
  5. Daniella — 624 tokens.
  6. Clara — 493 tokens.
  7. Orlee — 661 tokens.
  8. Marcus — 614 tokens.
  9. Michelle — 655 tokens.
  10. Michael — 637 tokens.

Read the 2024 source

2025 edition
  1. Claire — 650 tokens.
  2. Alexander — 648 tokens.
  3. Barry — 645 tokens.
  4. Claire — 658 tokens.
  5. Isabelle — 653 tokens.
  6. Olivia — 659 tokens.
  7. Jinna — 624 tokens.
  8. Carrie — 646 tokens.
  9. Janna — 658 tokens.
  10. Emma — 788 tokens.

Read the 2025 source

2026 edition
  1. George — 635 tokens.
  2. Omar — 656 tokens.
  3. My (Im)Perfect Life — 640 tokens.
  4. Samantha — 648 tokens.
  5. Mitchell — 647 tokens.
  6. Zoe — 617 tokens.
  7. Hayk — 651 tokens.
  8. Jill — 700 tokens.
  9. Matthew — 654 tokens.
  10. Sarah — 644 tokens.

Read the 2026 source

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