Scientific Practices, Engineering Design, and Crosscutting Concepts · 19 min read
Scientific Practices
Domain 1 questions almost never ask about practices in the abstract. Instead, they place you inside a real situation, such as a student investigation of plant growth, a data table of earthquake magnitudes, or a design challenge for a water filter, and ask you to identify a variable, judge a conclusion, spot a flaw, or choose the next step. This section teaches the three competencies of the domain in that spirit. Chapter 1.1 covers scientific practices, Chapter 1.2 covers engineering design, and Chapter 1.3 covers the seven crosscutting concepts, each illustrated with physical, life, and earth science examples.
This chapter covers Competency 0001 (SMR 1.1): asking questions that can be investigated, planning and conducting safe investigations, choosing tools, analyzing data with mathematics and statistics, handling error, using and refining models, and building and evaluating evidence-based explanations. It is the biggest chapter in this guide because these skills appear in nearly every Domain 1 question and also support the other three domains.
Asking Questions That Can Be Investigated
Science begins with a question, but not every question can be answered by science. A scientifically testable question asks about something that can be observed or measured, and it can be answered by collecting evidence. "Does the temperature of water affect how fast sugar dissolves?" is testable. "Is sugar better than salt?" is not, because "better" is a value judgment with no measurable meaning until it is defined.
A strong investigable question usually names two things: the factor you will change and the outcome you will measure. A useful sentence frame is "How does [independent variable] affect [dependent variable]?" For example, "How does the angle of a ramp affect the speed of a cart at the bottom?" or "How does the amount of light affect the number of algae cells in a sample after five days?"
From the question comes a hypothesis, a tentative, testable explanation, and a prediction, a specific statement of what should happen if the hypothesis is correct. Teachers often blur these. A hypothesis explains ("Warmer water molecules move faster, so they collide with sugar crystals more often and break them apart sooner"); a prediction forecasts ("If I dissolve sugar in water at 60 °C, it will dissolve faster than in water at 20 °C").
Common Trap: A hypothesis is not "an educated guess" about any outcome. On the exam, the best hypothesis choice is the one that is testable and offers a mechanism or relationship, not the one that merely states a preference or an untestable claim.
Variables, Controls, and Experimental Design
Every controlled experiment has three kinds of variables.
A control group (or control condition) is a baseline for comparison. In a fertilizer study, plants that receive no fertilizer are the control group; plants receiving fertilizer are the experimental groups. Note the difference: a controlled variable is a factor held constant, while a control group is a condition used for comparison.
Worked Example: A student wants to know whether the mass of a pendulum bob affects its period. She hangs bobs of 50 g, 100 g, and 200 g from the same 1.0 m string, releases each from a 10° angle, and times 10 swings, repeating each mass three times.
Result: Mass is independent, period is dependent, and length and angle are controlled. (Physics predicts the period will not change noticeably with mass, an excellent example of a null result that is still valuable evidence.)
Good design also includes repeated trials, adequate sample size, and randomization. Repeated trials reduce the effect of random error. A large sample makes it less likely that a few unusual individuals distort the result; testing one plant per group is a serious flaw. Random assignment of subjects to groups prevents hidden bias, such as putting the healthiest seedlings into the fertilizer group.
Not every investigation is a controlled experiment. Observational studies, field studies, and long-term monitoring are essential when variables cannot be manipulated. A geologist cannot change the magnitude of an earthquake, and an ecologist cannot ethically remove a predator from a whole forest to see what happens. These studies can reveal patterns and correlations, but they need careful reasoning before drawing causal conclusions.
Exam Tip: When a question asks for the best way to improve an investigation, look first for an uncontrolled variable, a missing control group, a sample size of one, or a lack of repeated trials. Those are the most frequently tested flaws.
Laboratory Safety
As a future teacher, you are responsible for safety, and the exam expects you to know standard precautions and the reasoning behind them.
Wear chemical splash goggles whenever chemicals, heat, or projectiles are involved; know where the eyewash station and safety shower are, and flush eyes for at least 15 minutes after a splash.
Read the Safety Data Sheet (SDS) for each chemical to learn its hazards, handling, storage, and first-aid measures.
Always add acid to water slowly, never water to acid, because diluting concentrated acid releases heat that can cause boiling and splattering.
Use a fume hood for volatile or toxic substances; waft odors toward your nose rather than sniffing directly.
Tie back long hair, secure loose clothing, and wear closed-toe shoes.
Store chemicals by compatibility (for example, oxidizers away from flammables), not alphabetically.
Dispose of chemicals according to the SDS and local rules; never pour heavy metals or organic solvents down the drain.
Treat biological materials with care: wash hands, disinfect surfaces, and never culture unknown microbes at body temperature.
Report every accident or broken glassware to the teacher; use a brush and dustpan, not hands, for broken glass.
Memory Aid: "Do what you oughta, add acid to water." The rhyme reminds you which liquid goes into which.
Tools, Units, and Technology
Choosing the right tool is a frequent exam task. The best tool matches the quantity and the needed precision.
Common Laboratory Tools and What They Measure
| Tool | Quantity Measured | Notes |
|---|---|---|
| Electronic balance | Mass (g) | Tare the container first |
| Graduated cylinder | Liquid volume (mL) | Read the bottom of the meniscus at eye level |
| Volumetric pipette or buret | Precise liquid volume | Used in titrations |
| Beaker | Rough volume only | Holding and mixing, not measuring |
| Thermometer or temperature probe | Temperature (°C) | Probes log data over time |
| Spring scale or force sensor | Force (N) | Weight is a force, not a mass |
| Metric ruler or calipers | Length (cm, mm) | Calipers for small objects |
| pH meter or indicator paper | Acidity (pH) | Meter is more precise |
| Compound light microscope | Small structures | Total magnification = ocular × objective |
Scientists use the International System of Units (SI). The base units most often tested are the meter (length), kilogram (mass), second (time), kelvin (temperature), mole (amount of substance), and ampere (current). Prefixes scale units by powers of ten.
Common Metric Prefixes
| Prefix | Symbol | Factor |
|---|---|---|
| kilo | k | 10³ |
| centi | c | 10⁻² |
| milli | m | 10⁻³ |
| micro | μ | 10⁻⁶ |
| nano | n | 10⁻⁹ |
Technology extends the senses. Data loggers and probeware record temperature, pH, motion, or dissolved oxygen many times per second. Satellites track sea surface temperatures and vegetation; seismometer networks locate earthquakes; DNA sequencers read genetic code; spreadsheets and simulations analyze data sets too large to handle by hand. On the exam, a choice involving a probe or data logger is often correct when continuous, rapid, or long-term measurements are needed.
Accuracy, Precision, and Error
Accuracy describes how close a measurement is to the true or accepted value. Precision describes how close repeated measurements are to each other. A dartboard picture helps: darts tightly grouped but off-center are precise but not accurate; darts scattered evenly around the bullseye are accurate on average but not precise.
Errors come in two main types. Random error causes unpredictable scatter above and below the true value, from things like reaction time with a stopwatch or small fluctuations in a reading. Repeated trials and averaging reduce random error. Systematic error shifts every measurement in the same direction, from causes such as a balance that is not zeroed, a miscalibrated thermometer, or always reading a meniscus from above. Averaging does not remove systematic error; recalibrating the instrument or fixing the method does.
Common Trap: Students often claim that "doing more trials" fixes every error. More trials improve precision and reduce random error, but a thermometer that reads 2 °C too high will still be 2 °C too high after a hundred trials.
Percent error compares an experimental value with an accepted value:
percent error = |experimental − accepted| ÷ accepted × 100%
Worked Example: A class measures the density of an aluminum block as 2.61 g/cm³. The accepted value is 2.70 g/cm³. Find the percent error.
Result: The percent error is about 3.3%.
Significant Figures
Significant figures communicate the precision of a measurement. A measurement includes all certain digits plus one estimated digit. The rules:
All nonzero digits are significant (345 has three).
Zeros between nonzero digits are significant (305 has three).
Leading zeros are never significant (0.0045 has two).
Trailing zeros after a decimal point are significant (4.50 has three; 0.00450 has three).
Trailing zeros in a whole number without a decimal point are ambiguous (1,200 may have two, three, or four); scientific notation removes the doubt (1.20 × 10³ has three).
Exact numbers, such as counted objects or defined conversions (100 cm = 1 m), have unlimited significant figures.
Calculation rules differ for the two kinds of operations. For multiplication and division, round the result to the fewest significant figures in any factor. For addition and subtraction, round the result to the fewest decimal places in any term.
Worked Example: A rectangle measures 4.56 cm by 1.4 cm. A second problem adds 12.11 g and 0.3 g.
Result: The area is 6.4 cm² and the total mass is 12.4 g.
Exam Tip: An answer with many digits copied from a calculator display usually signals false precision. When choices differ only in the number of digits, apply the significant-figure rules.
Statistics, Probability, and Variation
Data from living systems, weather, and even careful physics labs vary. Statistics describe that variation.
You do not need to perform long standard deviation calculations on the exam, but you should understand what the number means. In a roughly bell-shaped (normal) distribution, about 68% of values fall within one standard deviation of the mean and about 95% fall within two.
Worked Example: Five trials of a cart rolling down a ramp give times of 12.1 s, 12.4 s, 11.9 s, 12.2 s, and 12.4 s. Find the mean and describe the spread.
Result: The mean is 12.2 s with a standard deviation of about 0.2 s, showing precise data.
Probability expresses how likely an outcome is, from 0 (impossible) to 1 (certain). It appears in genetics (a 1/4 chance of a recessive phenotype from two heterozygous parents), in radioactive decay (each nucleus has a fixed chance of decaying per unit time), and in hazard forecasting (a given percent chance of a large earthquake within a span of decades). Larger samples bring observed frequencies closer to predicted probabilities, which is one more reason sample size matters.
Analyzing and Interpreting Data and Graphs
Choose the graph to fit the data:
When reading a graph, check the title, the axes and their units, and the scale. Then describe the trend: increasing, decreasing, leveling off, or cyclic. Recognize common shapes. A straight line through the origin shows direct proportion (y = kx). A curve that falls as x increases, such as gas volume versus pressure, shows an inverse relationship. A curve that rises faster and faster shows exponential growth, as in a bacterial population with unlimited resources. A curve that rises and levels off suggests a limiting factor or carrying capacity.
The slope of a line often has physical meaning. On a distance-time graph, slope is speed. On a velocity-time graph, slope is acceleration. On a mass-volume graph, slope is density.
Worked Example: A best-fit line on a graph of mass versus volume passes through (10 cm³, 27 g) and (40 cm³, 108 g). What is the density of the material?
Result: The density is 2.7 g/cm³, consistent with aluminum.
Interpolation estimates a value between measured points and is usually reliable. Extrapolation extends a trend beyond the data and is risky, because the relationship may change. A spring obeys a straight-line pattern only until it is stretched past its elastic limit.
Common Trap: Correlation is not causation. Ice cream sales and drowning incidents both rise in summer, but one does not cause the other; a third factor (hot weather) drives both. A causal claim requires a controlled experiment or a well-supported mechanism plus consistent evidence from many studies.
Mathematics and Computational Thinking
Dimensional analysis (the factor-label method) converts units by multiplying by conversion factors written as fractions equal to 1, arranged so unwanted units cancel. It also checks your work: if the units of the result are wrong, the setup is wrong.
Worked Example: A car travels at 65 miles per hour. Convert this speed to meters per second (1 mile = 1,609 m; 1 hour = 3,600 s).
Result: The speed is about 29 m/s.
Worked Example: A teacher needs the mass of 250. mL of ethanol for a demonstration. The density of ethanol is 0.789 g/mL.
Result: The mass is about 197 g.
Proportional reasoning recognizes how one quantity changes when another changes. If y is directly proportional to x, doubling x doubles y. If y is inversely proportional to x, doubling x halves y. If y depends on x², doubling x makes y four times as large; this is why doubling a car's speed quadruples its kinetic energy and its braking distance. If y depends on 1/x², as with gravity or light intensity, doubling the distance cuts y to one fourth.
Computational thinking includes breaking a problem into steps, writing algorithms, using spreadsheets to process data, and running simulations. Simulations let scientists test models of systems that are too large, slow, dangerous, or expensive to study directly, such as climate over centuries, the spread of a disease, or the collision of galaxies. A simulation is only as good as its assumptions, so scientists compare its output with real observations.
Developing, Using, and Refining Models
A scientific model is a representation of a system that helps explain or predict its behavior. Models can be physical (a globe, a DNA kit), conceptual (a diagram of the water cycle), mathematical (F = ma), or computational (a climate simulation). Every model simplifies reality, so every model has limitations. A good teacher, and a good test taker, can say what a model shows well and what it leaves out.
Science changes models when new evidence appears. The history of the atomic model is the classic example, and the exam may ask which evidence led to which change.
How the Atomic Model Changed with Evidence
| Model | Key Idea | Evidence That Led to Change |
|---|---|---|
| Dalton | Atoms are tiny indivisible spheres; each element has its own kind | Laws of conservation of mass and definite proportions |
| Thomson | "Plum pudding": electrons embedded in positive matter | Cathode ray experiments revealed the electron |
| Rutherford | Tiny dense positive nucleus; mostly empty space | Most alpha particles passed through gold foil, a few bounced back |
| Bohr | Electrons in fixed energy levels around the nucleus | Hydrogen's line spectrum showed specific energies |
| Quantum (electron cloud) | Electrons in probability regions called orbitals | Wave behavior of electrons; Bohr model failed for larger atoms |
Notice the pattern: each model explained the evidence available, and each was revised, not thrown away entirely, when new evidence did not fit. Other examples include the shift from an Earth-centered (geocentric) to a Sun-centered (heliocentric) solar system, supported by telescope observations such as the phases of Venus, and the development from continental drift to plate tectonics, which became widely accepted only after seafloor spreading and magnetic striping supplied a mechanism.
Memory Aid: For the atomic model sequence, remember "Dalton's Ball, Thomson's Pudding, Rutherford's Nucleus, Bohr's Orbits, Quantum Cloud."
Exam Tip: Be careful with the words theory and law. A scientific law describes a pattern, often mathematically (the law of conservation of mass). A scientific theory is a well-tested explanation of why something happens (atomic theory, the theory of evolution). Theories do not "graduate" into laws; they do different jobs.
Constructing Explanations and Arguing from Evidence
A scientific explanation links evidence to a scientific principle. The claim-evidence-reasoning (CER) framework is widely used in California classrooms and is a natural structure for constructed responses.
Worked Example: Students place identical ice cubes on a metal plate and a plastic plate at room temperature. After 5 minutes, the ice on the metal plate has lost 18 g and the ice on the plastic plate has lost 4 g. Construct an explanation.
Result: A complete CER explanation states the claim, quantifies the evidence, and names conduction as the principle.
Evaluating an argument means asking whether the evidence is sufficient (enough trials, large enough sample), relevant (does it actually address the claim), and reliable (were variables controlled, is the source credible). It also means considering alternative explanations. Scientific arguments are strengthened by peer review, replication by independent investigators, and transparent reporting of methods and uncertainty.
Obtaining, Evaluating, and Communicating Information
Scientists and teachers must judge sources. Peer-reviewed journals, government science agencies, and university research groups are generally reliable. Be cautious with sources that sell a product, lack an author or methods, cherry-pick data, or rely on testimonials. A single study, especially a small one, is weaker evidence than a consistent body of research.
Communication uses tables, graphs, diagrams, written reports, and presentations. A clear lab report states the question, hypothesis, materials and procedure (detailed enough to replicate), data, analysis, conclusion, and sources of error. Results are communicated honestly, including results that do not support the hypothesis.
Key Takeaways
A testable question names a measurable independent and dependent variable; controlled variables and a control group make comparisons fair.
Accuracy is closeness to the true value; precision is closeness of repeated measurements. Repetition reduces random error but not systematic error.
Apply significant-figure rules: fewest significant figures for multiplying and dividing, fewest decimal places for adding and subtracting.
Use dimensional analysis to convert units, and read slopes and curve shapes on graphs for physical meaning; correlation alone does not prove causation.
Models simplify reality and are revised as evidence accumulates, as the atomic model shows from Dalton to the quantum model.
A strong explanation follows claim, evidence, and reasoning, uses specific data with units, and considers alternative explanations.
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