Study guide
4.9 Scientific Reasoning and the Scientific Method
ATI TEAS 7 Nursing Admission
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1 Introduction to the ATI TEAS 7
1.1 About the ATI TEAS 7 Exam 1.2 Content Area Breakdown 1.3 Scoring and Program Requirements 1.4 Building Your TEAS Study Plan
2 Reading
2.1 Identifying the Main Idea 2.2 Making Inferences and Drawing Conclusions 2.3 Summarizing and Paraphrasing 2.4 Understanding Text Structure 2.5 Author's Purpose, Tone, and Point of View 2.6 Evaluating Arguments, Evidence, and Credibility 2.7 Integrating Information and Interpreting Graphics 2.8 TEAS Reading Strategies, Pacing, and Practice
3 Mathematics
3.1 Whole Numbers and Basic Operations 3.2 Fractions 3.3 Decimals and Percentages 3.4 Ratios and Proportions 3.5 Algebra and Equations 3.6 Measurement and Unit Conversions 3.7 Statistics and Data Interpretation 3.8 Geometry Basics
4 Science
4.1 The Cardiovascular System 4.2 The Respiratory System 4.3 The Digestive System 4.4 The Nervous System 4.5 The Musculoskeletal System 4.6 The Urinary and Reproductive Systems 4.7 Cell Biology and Genetics 4.8 Chemistry Basics 4.9 Scientific Reasoning and the Scientific Method
5 English & Language Usage
5.1 Grammar and the Parts of Speech 5.2 Sentence Structure 5.3 Punctuation 5.4 Spelling and Word Usage 5.5 Vocabulary in Context and Word Parts 5.6 Paragraph Organization and the Writing Process 5.7 Formal vs. Informal Language, Audience, and Tone 5.8 TEAS English Section Strategies and Pacing
4.9 Scientific Reasoning and the Scientific Method
Key Takeaways Explain This Topic AI
The scientific method moves from observation to question to testable hypothesis to experiment to analysis to conclusion
The independent variable is manipulated, the dependent variable is measured, and controlled variables are held constant
A control group provides a baseline; randomization, blinding, and a placebo reduce bias; larger samples are more reliable
Correlation does not prove causation—watch for confounding variables before concluding cause and effect
Validity means a study measures what it intends to (accurate); reliability means repeated measurements agree (consistent)
Last updated: June 2026
Thinking Like a Scientist
The ATI TEAS 7 Science section reserves a block of questions for scientific reasoning — not memorized facts, but the ability to design a fair test, identify variables, read a graph, and judge whether a claim is justified. Nurses use these skills constantly to read research, interpret monitor data, and avoid jumping to conclusions.
The Scientific Method
- Step What You Do Example
- 1. Observation Notice a phenomenon Patients with low vitamin D report fatigue
2. Question Ask why or how Does vitamin D reduce fatigue?
- 3. Hypothesis Propose a testable, falsifiable answer If patients take vitamin D, fatigue will decrease
- 4. Experiment Test it under controlled conditions Randomized controlled trial
- 5. Analyze data Examine results, often statistically Compare fatigue scores between groups
- 6. Conclusion Support or reject the hypothesis Evidence does or does not support it
- 7. Communicate Share findings for peer review Publish in a journal
A strong hypothesis is testable and falsifiable — phrased as an "if/then" prediction that an experiment could prove wrong. "Plants are pretty" is not a hypothesis; "if plants get more light, then they grow taller" is.
Variables in an Experiment
- Variable Definition Example
- Independent What the researcher changes Vitamin D dose
- Dependent What the researcher measures (the outcome) Fatigue score
- Controlled Held constant for a fair test Age, diet, sleep
- Confounding An uncontrolled variable that distorts results Exercise level
A memory hook: the dependent variable depends on the independent variable. In a graph, the independent variable goes on the x-axis and the dependent variable on the y-axis .
Experimental Design
- Component Purpose
- Control group Baseline that receives no treatment
- Experimental group Receives the treatment
- Randomization Reduces selection bias
- Blinding Subjects do not know their group
- Double-blind Neither subjects nor researchers know
- Placebo Inactive treatment for the control group
- Sample size Larger samples give more reliable results
The control group is the heart of a fair experiment: without an untreated baseline, you cannot tell whether the treatment or something else caused the change.
Correlation vs. Causation
Correlation means two variables move together; causation means one directly produces the other. The TEAS repeatedly tests the rule that correlation does not prove causation.
Worked Example: A researcher notices that as monthly ice cream sales rise, drowning deaths also rise, and the two are strongly correlated. Does eating ice cream cause drowning? No. A confounding variable — hot summer weather — independently increases both ice cream sales and swimming (and therefore drowning). Two variables can rise together without one causing the other. To claim causation you need a controlled experiment that rules out confounders.
Types of Studies
- Study Description Strength
- Experimental Researcher manipulates a variable Can show causation
- Observational Researcher only observes Cannot prove causation
- Case study In-depth look at one subject Rich detail, not generalizable
- Survey Self-reported data Fast, large samples
- Longitudinal Follows subjects over time Shows change and development
Only a controlled experiment can establish cause and effect, because only there does the researcher actively manipulate the independent variable while holding others constant.
Reading Data and Graphs
Match the graph to the data:
- Bar graph — compares categories (e.g., fatigue scores by treatment group)
- Line graph — shows change over time or a trend
- Pie chart — shows parts of a whole (percentages)
- Scatter plot — shows the relationship/correlation between two variables
Always read the title, axis labels, and units first, then identify the trend before drawing any conclusion.
Measurement and Units
Science uses the SI (metric) system . Prefixes scale a base unit by powers of ten: kilo- (1,000x), centi- (1/100), milli- (1/1,000), micro- (1/1,000,000). So 1 kilogram = 1,000 grams and 1 milliliter = 0.001 liter.
Validity, Reliability, Accuracy, and Precision
- Term Meaning
- Validity The study measures what it claims to measure
- Reliability Repeated measurements give consistent results
- Accuracy A measurement is close to the true value
- Precision Repeated measurements are close to each other
A classic distinction: a scale that always reads 5 pounds heavy is precise (consistent) but not accurate (off from the true value). A study can be reliable (repeatable) yet still invalid if it measures the wrong thing — for example, using shoe size to estimate intelligence would be reliable but not valid.