How do you draw a valid conclusion from data, and how do error and bias limit what you can claim?
Construct and critique conclusions and explanations: make a claim supported by evidence and reasoning, judge whether the data support the hypothesis, and identify sources of error and uncertainty in an investigation (Virginia 2018 Biology SOL BIO.1.d).
A SOL-level answer on conclusions for the Virginia Biology EOC: claim, evidence and reasoning, deciding whether data support a hypothesis, distinguishing correlation from causation, and identifying sources of error and uncertainty.
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What this topic is asking
Virginia Biology SOL standard BIO.1.d is about constructing and critiquing conclusions and explanations. A conclusion is not just "what happened"; it is a claim about the relationship between the variables, backed by evidence from the data and reasoning that connects them. The Biology EOC also asks you to judge whether the data actually support the hypothesis, to tell a correlation apart from a cause, and to spot sources of error. These critical-thinking items appear across every reporting category.
Claim, evidence, and reasoning
For example: "The fertilizer increased growth (claim). The treated plants grew on average 8 cm taller than the untreated control (evidence). The extra nutrients let the plants build more tissue, so they grew more (reasoning)." A conclusion that states only the claim, with no data, earns little credit, because the SOL rewards arguing from evidence.
Does the data support the hypothesis?
A hypothesis is supported if the data show the predicted relationship and refuted if they do not. Either outcome is a valid scientific result. A refuted hypothesis is not a failed experiment; learning that a factor has no effect is useful information. The SOL may give you a hypothesis and a data set and ask whether the data support it, so compare what was predicted with what the data actually show, including the control.
Correlation is not causation
This is one of the most tested ideas in scientific reasoning. The classic example is that ice cream sales and sunburn both rise in summer; neither causes the other, because both are driven by hot, sunny weather. When a question reports that two things "are linked" from observational data, be cautious about claiming causation.
Sources of error and uncertainty
No measurement is perfect, and no investigation controls everything. Sources of error include the limits of the measuring instrument, human reaction time, variation between organisms, and variables that were not fully controlled. Uncertainty is the range within which the true value probably lies. A good investigation reduces error by controlling variables, repeating trials, using a larger sample, and using more precise instruments. The SOL expects you to identify a realistic source of error and to suggest how the design could be improved.
Try this
Q1. A newspaper reports that students who eat breakfast score higher on tests, so it claims breakfast causes higher scores. Why is this conclusion not fully justified? [2]
- Cue. The data show a correlation, not causation; a third factor (such as a stable home routine) could cause both, so a controlled study is needed before claiming breakfast causes the higher scores.
Q2. State the three parts of a complete conclusion. [1]
- Cue. Claim, evidence, and reasoning.
Exam-style practice questions
Practice questions written in the style of VDOE exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
VA Biology SOL (2023 released style)1 marksA study finds that towns with more ice cream sales also have more cases of sunburn. Which conclusion is most scientifically valid? (A) Ice cream causes sunburn. (B) Sunburn causes people to buy ice cream. (C) Both rise in summer, so the link is a correlation, not cause and effect. (D) Ice cream prevents sunburn.Show worked answer →
A 1-point multiple-choice item on correlation versus causation.
The correct answer is C. The two rise together because both are linked to a third factor, hot, sunny weather, so this is a correlation, not evidence that one causes the other. A and B claim causation the data do not support, and D is contradicted by the data.
The test rewards recognizing that a correlation between two variables does not prove that one causes the other.
VA Biology SOL (2024 released style)2 marksAn investigation tested whether a plant food increases growth. The treated plants grew taller than the untreated control. (a) State a valid conclusion that uses the evidence. (b) Identify one source of error that could make the result less reliable.Show worked answer →
A 2-point item on claim, evidence, and error.
(a) 1 point: a conclusion that links claim to evidence, for example "The plant food increased growth, because the treated plants grew taller on average than the untreated control group." The point requires both the claim and the supporting evidence.
(b) 1 point: any reasonable source of error, such as differences in starting plant size, uneven light or water, measurement error in reading height, or too small a sample size, with the idea that it could affect the result.
Markers reward a conclusion grounded in the comparison to the control and a genuine source of error or uncertainty.
Related dot points
- Plan and carry out controlled investigations: ask a testable question, form a hypothesis relating an independent and a dependent variable, identify the variables that must be controlled, and explain the role of the control group (Virginia 2018 Biology SOL BIO.1.a, BIO.1.b).
A SOL-level answer on experimental design for the Virginia Biology EOC: testable questions, hypotheses, independent, dependent, and controlled variables, the control group, and why a valid design isolates one variable at a time.
- Construct and interpret data tables and graphs: organize data, choose an appropriate graph type, read trends and values from a graph, and calculate simple quantities such as means and rates from data (Virginia 2018 Biology SOL BIO.1.c).
A SOL-level answer on organizing and interpreting data for the Virginia Biology EOC: building data tables, choosing line, bar, and scatter graphs, reading trends, and calculating means and rates from data.
- Develop and use models to explain and predict, judging their merits and limitations, and obtain, evaluate, and communicate scientific information from reliable sources (Virginia 2018 Biology SOL BIO.1.e, BIO.1.f).
A SOL-level answer on scientific models and communication for the Virginia Biology EOC: what models are and their limits, the difference between a hypothesis, theory, and law, and how to evaluate and communicate reliable scientific information.
- Explain how the role of variation and mutations drives natural selection, producing adaptation and changing the heritable traits of a population over generations (Virginia 2018 Biology SOL BIO.7.b).
A SOL-level answer on natural selection for the Virginia Biology EOC: variation and mutations as the raw material, overproduction and competition, differential survival and reproduction (fitness), and how selection produces adaptation and shifts allele frequencies, with antibiotic resistance as the worked example.
- Explain the germ theory of infectious disease, the evidence that supports it, how pathogens are transmitted, and how the spread of disease can be prevented (Virginia 2018 Biology SOL BIO.4.e).
A SOL-level answer on germ theory for the Virginia Biology EOC: the idea that microorganisms cause disease, the evidence behind it, how pathogens spread, and how vaccines, hygiene, and antibiotics prevent and control disease.
Sources & how we know this
- 2018 Science Standards of Learning (Biology) — Virginia Department of Education (2018)
- SOL Practice Items (All Subjects) — Virginia Department of Education (2024)