HS-LS3-3: Probability and Trait Variation
Also called NGSS HS-LS3-3 · Topic: Inheritance and Variation of Traits · Life Science
The Official Wording
Apply concepts of statistics and probability to explain the variation and distribution of expressed traits in a population.
Clarification statement: Emphasis is on the use of mathematics to describe the probability of traits as it relates to genetic and environmental factors in the expression of traits.
Assessment boundary: Assessment does not include Hardy-Weinberg calculations.
NGSS Lead States, 2013 · Read it on nextgenscience.org · checked October 15, 2026
What HS-LS3-3 Means in Plain English
Students use probability and simple statistics to explain why traits show up in the numbers they do. They predict ratios from crosses, compare predictions with real counts, and use averages, ranges and graphs to describe how a trait like height spreads across a population.
They also show how environment shifts a trait, such as plant height with sunlight. Hardy-Weinberg equations are not part of this standard. It builds on MS-LS3-2 and 7.SP.7 probability models and prepares students for HS-LS4-3.
HS-LS3-3 I Can Statements
- I can predict trait ratios from a cross using probability.
- I can compare expected and observed counts in real data.
- I can explain how genes and environment both shape traits.
HS-LS3-3 Worked Example
A counting investigation with genetic corn ears from a cross between two parents that each carry a hidden yellow allele.
Do the purple and yellow kernels on a second-generation corn ear match the 3 to 1 ratio that probability predicts?
Set It Up: Genetic corn ears from a single-gene cross, pushpins to mark the first counted row, and a class data table.
- Predict the ratio: both parents carry one purple and one yellow allele, and purple is dominant.Expected: 3 purple to 1 yellow, 75% purple
- Count every kernel on the ear, row by row, marking the starting row with a pushpin.Ear 1: 312 purple, 98 yellow
- Turn the count into a percentage.312 ÷ 410 = 76% purple
- Pool counts from all eight ears in the class.2,436 purple, 812 yellow, 75.0% purple
- Compare the spread between single ears with the pooled result.Single ears range from 72% to 79%
Single ears came close to 75% purple, and the pooled class total matched the prediction almost exactly.
Why it works: Each kernel is a separate chance event with a 3 in 4 probability of purple, so small samples wander while large samples settle near the expected ratio.
Common Mistakes with HS-LS3-3
Expecting exact ratios in every sample
“Our ear is 72% purple, so the prediction is wrong”
Try this: Small samples vary by chance. Pool more data and check whether the totals move closer to the predicted ratio.
Reading a Punnett square as a family
“If they have four kids, exactly one will have the recessive trait”
Try this: A Punnett square gives the probability for each child separately, so a family of four can have zero, one or several.
Leaving out the environment
“Tall plants just have tall genes”
Try this: Grow plants with the same genes in different light or soil and compare, since conditions can shift a trait's average.
Where HS-LS3-3 Fits
Practice HS-LS3-3
Quick Check
- Two parents each carry one dominant and one recessive allele. What fraction of offspring should show the recessive trait?
- Why do bigger samples match predicted ratios better?
- Identical twins raised apart differ in height. What does that show?