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The Harbor City Ledger

Knowledge • Discovery • UnderstandingSunday, June 21, 2026Reading Edition

After a Science Fair Uproar, District Issues New Rules on “Good Enough” Experiments

A pilot “design clinic” turns student projects into a lesson on treatments, replication and what small studies can — and can’t — claim

SCIENCE & EDUCATION

HARBOR CITY — Monday, January 30, 2026

By Marisol Vega

Students at a Harbor City library workshop set up identical cups of bean seedlings under measured light treatments.

What began as a dispute over a wilted tray of bean seedlings at Harbor City Middle School’s science fair has ended with a new district guide on experimental design — one that emphasizes practical choices researchers make when money, time and classroom space are limited.

The controversy started after judges disqualified seventh-grader Alina Cho’s project on plant growth, citing “insufficient replication” and “unclear treatment levels,” according to a written decision shared with parents. Cho’s family appealed, saying the project was “penalized for being small.”

Within two weeks, the Harbor City Unified School District convened a Saturday “design clinic” at the public library, bringing together teachers, students and a visiting horticulture specialist from the county extension office.

“We’re not trying to turn kids into statisticians,” said Dr. Priya Nand, the extension specialist who led the session. “We’re trying to make sure they can answer the question they say they’re asking.”

The fight over one plant tray

Cho’s original setup used a single seedling under a desk lamp and a single seedling by a window. When the lamp bulb burned out midway through the project, she replaced it with a brighter one — and recorded the change.

“That’s what happened in real life,” Cho said in an interview. “I wrote it down. I thought being honest mattered.”

It did, judges said — but the change also meant the “light treatment” was no longer consistent.

“An experiment can be honest and still be uninterpretable,” said science teacher Adam Rios, who served as one of the judges. “We couldn’t tell whether any difference was from intensity, bulb type, heat, or just one plant being weird.”

Parents complained the fair had become “too strict,” while teachers said the judge’s feedback exposed a gap in how projects were taught.

The new guide: treatments first, then replication

At the design clinic, participants worked through a district handout that begins with a blunt rule: define the independent variable (IV) as something you can hold steady and vary on purpose.

The guide recommends that students choose “levels” of the IV that are meaningfully different rather than barely changed.

“Two levels can be enough if they’re distinct and controlled,” Nand told the group, gesturing at three light bulbs on the table. “But if you do three or four levels, you can start seeing whether the response is gradual or there’s a threshold.”

The handout also distinguishes between “levels” and “treatments.” A level is the amount (for example, low, medium, high light). A treatment is the full package applied to an experimental unit — meaning the light level plus the fact that everything else is kept the same.

In practice, Nand said, that means writing down the details students usually skip: distance from the lamp, how long lights stay on, pot size, soil type, watering schedule, and even which shelf the plants sit on.

“If you can’t describe it so someone else could repeat it, you don’t have a treatment — you have a vibe,” she said, drawing laughter.

Sample size vs. replication: what “more” actually means

The most contentious part of the clinic was also the most concrete: how many plants to grow.

The district guide urges students to prioritize replication — multiple experimental units per treatment — over measuring the same unit again and again.

“Measuring one plant for 20 days does not make it 20 plants,” Rios told attendees. “It makes it one plant with 20 measurements.”

Repeated measurements can be valuable, Nand added, but they answer a different kind of question: how the same individual changes over time.

“When you replicate, you’re asking if the effect shows up across multiple seedlings,” she said. “That’s what lets you say it’s not just an oddball plant.”

In the district’s language, replication is what supports a claim about a treatment’s typical effect, while repeated trials over time — running the whole experiment again — support a claim that the effect is robust under slightly different conditions.

Repeated trials and the reality of classrooms

One compromise in the new guide addresses what teachers said was the biggest constraint: limited space.

Instead of recommending students cram 50 pots onto one table, the guide suggests smaller experiments run twice.

“Two runs of a small study can be more convincing than one run where everything is crowded and messy,” said librarian and clinic host Elena Gutierrez, who offered shelf space in a sunny reading room for the pilot projects.

Still, the guide warns against calling repeated measurements “replicates,” and warns students not to change procedures between runs without labeling the change.

“If you change the bulb, that’s not a repeat,” Nand told students. “That’s a new treatment. It can be interesting — but it means you’re answering a new question.”

A decision framework for “good enough” design

The district’s handout includes a short decision framework that teachers said was designed for time-strapped students.

  • If resources are limited, prioritize clear treatments and basic replication before adding more IV levels.
  • If space is limited, prioritize fewer treatments with more replicates per treatment.
  • If time is limited, prioritize consistent measurement timing over more frequent measurements.
  • If equipment is unreliable, prioritize a stable, controllable IV (for example, fixed lamp distance) over a more precise but fragile setup.
  • If the goal is a pattern (dose-response), add levels only after each level has enough replicates to be believable.

Rios said the framework is intended to steer students away from “one of everything” designs.

“Kids want to do five light levels, three fertilizers, and different music,” he said. “Then they end up with one plant in each group and can’t interpret anything.”

A worked example: designing a small but credible light study

During the clinic, Nand walked students through a model experiment similar to Cho’s original idea — but structured to survive a classroom reality.

The question: How does light intensity affect bean seedling growth over two weeks?

The IV: light intensity, defined operationally by lamp distance and confirmed using a phone light-meter app.

Treatments (three levels):

  • Low light: lamp at 60 centimeters above the seedlings, 12 hours on per day.
  • Medium light: lamp at 30 centimeters, 12 hours on per day.
  • High light: lamp at 15 centimeters, 12 hours on per day.

To reduce heat differences, the guide recommends using the same bulb type and adding a small fan set to the same speed for all shelves, or choosing LED lamps that run cooler.

Replicates: eight seedlings per treatment, for 24 total, each planted in identical cups with the same soil and a single seed.

Experimental unit: one cup with one seedling.

Randomization: after sprouting, cups are assigned a number and placed on shelves using a simple random draw. Midway through the experiment, the cups are rotated within each shelf to reduce “edge effects.”

Controls held constant: soil mass, cup size, water volume, watering time, temperature range, and photoperiod.

Measurement schedule:

  • Day 0 (sprout day): record emergence date and initial height.
  • Days 3, 6, 9, 12, 14: measure height from soil line to the highest point; count true leaves.
  • Daily: record watering (milliliters) and any anomalies (fallen cup, bulb flicker).

Nand said the schedule was intentionally modest.

“Six measurement days is enough to see a curve without turning this into a full-time job,” she said.

Interpretability, she told students, would come from the combination of distinct treatments, adequate replication, and consistent procedures.

“With eight plants in each group, you can show spread — not just an average,” Nand said. “If one plant dies, you still have data. And if the high-light plants grow faster across most replicates, you can say it’s likely the treatment.”

What the new rules mean for claims

The district guide also instructs students to match conclusions to design.

A project with two treatments and low replication may be able to say, “In this setup, these seedlings grew taller under the lamp than by the window,” but not, “More light always makes plants grow faster,” Nand said.

“It’s not about policing ambition,” she said. “It’s about making sure the claim fits the evidence.”

For Cho, the clinic offered a chance to rebuild her project for a spring showcase. She left carrying a box of identical cups and a printed sheet labeled “Treatment Log.”

“I still want to do light,” she said. “Now I know what people mean when they say ‘replicates.’ It’s not just more days. It’s more plants.”

District science coordinator Lionel Park said the new guide will be rolled out to all middle schools next month, alongside a judge training session.

“We want students to learn that rigor isn’t the enemy of creativity,” Park said. “It’s how you make your idea understandable to someone else.”

Course
Introductory Biology Foundations: Cells, Genes, Evolution & Ecol
10 units46 lessons
Topics
Biology (introductory)Biochemistry (intro level)Cell & Molecular BiologyGeneticsEvolutionary BiologyEcology
About this course

Build a strong, integrated introduction to biology by learning core vocabulary and the cause–effect logic that connects biochemistry, cells, genes, evolution, and ecosystems. Focus on how science generates and tests explanations through hypotheses, experimental design (controls, independent/dependent variables), and evidence-based interpretation. Develop quantitative comfort with simple graphs, proportions, and frequency reasoning (including Hardy–Weinberg). Master key structure–function themes (macromolecules, enzymes, membranes, organelles), information flow (DNA→RNA→protein, mutations), energy transformations (ATP, respiration), inheritance (meiosis, Mendelian patterns), evolutionary mechanisms, and ecological energy flow and interactions.