- What potential flaw does this experimental setup introduce?
Answer: The scientist's investment in the product could lead to unintentional observer bias in assessment.
When you encounter questions about experimental design, focus on identifying factors that could compromise the validity or reliability of results. Scientific experiments must minimize bias to produce trustworthy conclusions.
The key issue here is observer bias – when a researcher's expectations or personal investment influences how they interpret data. Since the lead scientist who developed the cream is also evaluating the photos, they have a strong motivation to see positive results. Even with the best intentions, this creates a conflict of interest that could unconsciously influence their assessment of skin redness reduction. This is why answer D is correct – the scientist's investment in the product's success introduces a serious potential for biased evaluation.
Let's examine why the other options, while potentially problematic, aren't the primary flaw: Answer A identifies a real concern about application consistency, but this affects data quality rather than introducing systematic bias. Answer B correctly notes that a control group would strengthen the study, but its absence doesn't create the same direct conflict of interest as having the creator evaluate results. Answer C points out a technical issue with photo standardization that could affect measurements, but again, this is about data quality rather than observer bias.
Study tip for the GED: When analyzing experimental design, always look for conflicts of interest first. Questions often test whether you can identify when the person collecting or interpreting data has reasons to favor certain outcomes. Independent, blinded evaluation is a cornerstone of good scientific methodology.
- In this experiment, what are the independent and dependent variables, respectively?
Answer: The salt concentration; the average length of the shrimp.
When you encounter experimental design questions, focus on identifying what the researcher is deliberately changing versus what they're measuring as a result. This distinction is crucial for understanding scientific methodology.
In this experiment, the marine biologist is systematically varying the salt concentration across three levels (low, medium, and high) to see how this affects shrimp growth. The salt concentration is the independent variable — the factor the researcher controls and manipulates. The average length of the shrimp is the dependent variable — what she measures to see the effect of her manipulation. The shrimp length "depends on" the salt concentration she chooses.
Choice A correctly identifies salt concentration as independent and average shrimp length as dependent. This matches the experimental setup perfectly.
Choice B reverses these variables, which is a common mistake. Remember: you can't manipulate how long the shrimp grow and then measure what salt concentration results — the causation flows the other direction.
Choice C suggests the number of shrimp is the independent variable, but the passage states she uses 100 shrimp in each tank consistently. This is actually a controlled variable kept constant, not the manipulated factor.
Choice D identifies variables that aren't even mentioned as being varied in the experiment. The passage indicates "all other conditions" like temperature and food are kept the same, making these controlled variables, not the focus of study.
Study tip: For any experiment, ask yourself: "What is the researcher deliberately changing?" (independent) and "What are they measuring to see the effect?" (dependent). The independent variable comes first in time and causes changes in the dependent variable.
- In a scientific experiment, what is the primary purpose of a control group?
Answer: To provide a baseline for comparing the effects of the independent variable.
When you encounter questions about experimental design, focus on understanding what each component of an experiment is meant to accomplish. The control group is one of the most fundamental elements of scientific methodology.
A control group serves as your reference point - it's the group that doesn't receive the experimental treatment (independent variable) you're testing. This allows you to isolate the effect of your independent variable by comparing results between the experimental group (which receives the treatment) and the control group (which doesn't). Without this baseline, you couldn't determine whether any changes you observe are actually caused by your treatment or by other factors. This is exactly what option D describes.
Option A confuses control groups with proper documentation and methodology. While good experimental design does help with replication, the control group's specific purpose isn't to make experiments repeatable - that's achieved through clear procedures and detailed records.
Option B misunderstands experimental design principles. Control groups actually help you test one independent variable at a time, not multiple variables simultaneously. Testing multiple independent variables at once would make it impossible to determine which variable caused any observed effects.
Option C conflates control groups with sample size. While having adequate sample sizes (including enough subjects in your control group) does improve statistical power, the primary purpose of the control group itself is comparison, not increasing the number of trials.
Remember this key principle: in experimental design questions, always think about what allows scientists to make valid comparisons and draw reliable conclusions about cause and effect.
- Why is the lack of dietary information a potential flaw in this study?
Answer: Changes in diet could be a confounding variable that also affects blood pressure.
When analyzing scientific studies, you need to identify variables that could interfere with the results. A confounding variable is any factor other than what's being studied that could influence the outcome, making it unclear what actually caused any observed changes.
In this study, researchers want to determine if exercise affects blood pressure. However, diet is well-known to significantly impact blood pressure - high sodium intake raises it, while certain foods like those rich in potassium can lower it. Since the researchers didn't track participants' diets, they can't rule out dietary changes as an alternative explanation for any blood pressure changes they observe. This makes option D correct: changes in diet could be a confounding variable that also affects blood pressure.
The other options represent different types of study limitations but aren't confounding variables. Option A (timing of measurements) could introduce measurement error but wouldn't systematically bias results toward one group. Option B (different equipment brands) is an instrumentation issue that could affect accuracy but again wouldn't favor one group over another. Option C (age differences) would indeed be a confounding variable, but the question specifically asks why the lack of dietary information is problematic.
For GED science questions about experimental design, always ask yourself: "What other factors could explain the results besides what the researchers are testing?" Look for variables that could systematically affect the outcome and weren't properly controlled or measured. These confounding variables are often the key to identifying study flaws.
- In this study, what is the scientific role of Group 2?
Answer: They serve as the control group to account for the placebo effect.
When you encounter questions about scientific experiments, focus on identifying the key components: experimental groups (who get the treatment) and control groups (who provide the baseline for comparison).
In this allergy medication study, Group 2 serves as the control group. Their scientific role is to account for the placebo effect - the phenomenon where people experience symptom improvement simply because they believe they're receiving treatment. By giving Group 2 identical-looking sugar pills, researchers can measure how much improvement comes from psychological factors versus the actual medication. This allows them to determine if Group 1's results are truly due to the medication's effectiveness.
Let's examine why the other options miss the mark. Option B incorrectly identifies Group 2 as an experimental group testing sugar pill side effects - but sugar pills are inert and aren't being tested for their own effects. Option C suggests they're a "backup group" to confirm Group 1's results, but control groups don't confirm results; they provide comparison data. Option D calls them a "variable group" testing different medication dosages, but Group 2 receives no medication at all.
The correct answer is A - Group 2 serves as the control group to account for the placebo effect.
For GED Science success, remember that control groups are essential for valid experiments because they isolate the variable being tested. When you see experimental design questions, always identify what each group receives and ask: "What is this group's purpose in the overall design?"
- Based on the hypothesis that acid rain affects soil life, what is the dependent variable in this study?
Answer: The number of earthworms per square meter.
When you encounter a scientific study question, you need to identify the variables being tested. The dependent variable is what the researcher measures to see if it changes in response to different conditions - it's the outcome or effect being studied.
In this ecology study, the researcher wants to test whether acid rain affects soil life. She's looking to see if the number of earthworms changes based on different levels of acid exposure. The earthworm count is what she's measuring as her indicator of soil health - this makes the number of earthworms per square meter (A) the dependent variable.
Let's examine why the other choices don't work: The pH measurement (B) is actually another variable the researcher is measuring, but it's more of an indicator of acid rain exposure rather than the main outcome she's studying. The distance from the industrial area (C) is an independent variable - it's what the researcher is manipulating to create different conditions, not what she's measuring as a result. The sampling area size (D) is simply a constant in the experimental design - one square meter is used consistently across all locations to ensure fair comparison.
For GED science questions about experimental design, always ask yourself: "What is the researcher trying to prove changes?" That's usually your dependent variable. The thing being manipulated or varied by the researcher (like location/distance here) is typically the independent variable. This distinction appears frequently on the exam, so practice identifying cause versus effect in scientific studies.
- What is the most significant flaw in this experimental design?
Answer: There is no control group that did not receive the supplement.
When you encounter questions about experimental design, focus on the fundamental requirements for a valid scientific experiment: controlled variables, proper comparisons, and reliable measurement methods.
The most critical flaw here is the absence of a control group (D). Without a control group that receives no supplement or a placebo, you cannot determine whether the memory improvement was actually caused by the vitamin supplement. The improvement could result from practice effects (getting better at the test through repetition), placebo effects (feeling better because participants believe they're taking something helpful), or other uncontrolled factors. A proper experiment needs a comparison group to isolate the true effect of the treatment.
Let's examine why the other options, while potentially concerning, aren't the most significant flaws: Option A suggests 50 volunteers is too small, but this sample size can actually provide meaningful results if properly designed with appropriate controls. Option B claims one month is too short, but many supplement studies use similar or shorter timeframes, and the duration itself doesn't invalidate the design. Option C questions the accuracy of the memory test, but there's no evidence suggesting the test was flawed, and the same test was used both times.
For GED science questions about experiments, always ask: "What's missing that would make this a fair test?" The answer is usually a proper control group. Remember that correlation (supplement use coinciding with improvement) doesn't prove causation without controlling for other variables that could explain the results.
- The researcher's knowledge of the group assignments could lead to what type of experimental flaw?
Answer: Observer bias, where scoring is unconsciously influenced by expectations.
When you encounter questions about experimental design, focus on identifying potential sources of bias that could compromise the validity of results. This question tests your understanding of how researcher knowledge can introduce systematic errors.
The key issue here is that the researcher knows which participants received the energy drink versus the placebo, and this same researcher is scoring the concentration tests. This creates a perfect setup for observer bias, where the researcher's expectations unconsciously influence how they evaluate and score participants' performance. Even with the best intentions, knowing that a participant received the "supposed improvement" drink might lead the researcher to interpret borderline answers more favorably or notice better focus that isn't actually there.
Looking at the wrong answers: Choice A describes a sampling error, which relates to how participants are selected or assigned to groups, not the researcher's knowledge during evaluation. Choice B incorrectly suggests that using a look-alike drink prevents replication—actually, proper placebos enhance experimental validity and replicability. Choice C misidentifies this as a placebo effect in the control group, but the placebo effect would occur regardless of what the researcher knows, and the question specifically asks about the researcher's knowledge as the problem source.
The correct answer is D because observer bias directly results from the evaluator knowing group assignments while making subjective assessments.
For GED Science success, remember that proper experimental design requires blinding: participants shouldn't know their group assignment (single-blind), and ideally, researchers scoring results shouldn't know either (double-blind). When you see "researcher knows group assignments," immediately consider observer bias.
- What is the dependent variable in this experiment?
Answer: The increase in the length of the rods.
When you encounter questions about scientific experiments, you need to identify the different types of variables. The dependent variable is what the researcher measures to see the effect of their experiment—it's what "depends on" the changes being made.
In this thermal expansion experiment, the student is testing whether different metals expand by different amounts when heated. She's measuring how much each rod's length increases after heating. This length increase is what she's collecting as data to answer her research question, making it the dependent variable. Choice A correctly identifies this.
Let's examine why the other options are incorrect. Choice B (initial length and thickness) represents controlled variables—factors the student keeps constant to ensure a fair test. These aren't being measured for results. Choice C (temperature increase) is actually part of the independent variable—it's what the student controls and applies equally to all rods. The student sets this at 50°C for all samples. Choice D (type of metal) is the main independent variable—what the student deliberately changes to test its effect on expansion.
Remember this pattern: the dependent variable is always what gets measured as the outcome, while independent variables are what get changed or manipulated. Controlled variables are kept the same throughout. On GED Science questions about experiments, look for phrases like "measures," "records," or "observes" to spot the dependent variable—it's the data being collected to test the hypothesis.
- What is the independent variable in this experiment?
Answer: The amount of water given to each plant.
When you encounter experimental design questions, you need to identify what the researcher is deliberately changing versus what they're measuring or keeping constant. The independent variable is what the experimenter manipulates to test its effect.
In this experiment, the student is systematically changing the amount of water each plant receives - Plant A gets 50 mL, Plant B gets 100 mL, and Plant C gets 150 mL daily. This deliberate manipulation makes the amount of water the independent variable. The researcher controls this factor to see how it affects plant growth.
Looking at the wrong answers: Choice B (sunlight) is incorrect because all plants receive the same amount of sunlight - this is a controlled variable, not the independent variable. The passage explicitly states they're kept in the same location with identical light exposure. Choice C (final height) represents the dependent variable - this is what you measure to see the effect of your manipulation, not what you're changing. Choice D (type of plant) is wrong because all three plants are identical tomato plants, making this another controlled variable rather than the manipulated factor.
The correct answer is A - the amount of water is what the student deliberately varies between the three plants to test the hypothesis about water's effect on growth.
Study tip: Remember the acronym DRY MIX - Dependent Responding Y-axis, Manipulated Independent X-axis. The independent variable is always what the researcher intentionally changes or manipulates, while everything else should be controlled to ensure a fair test.
- Which of the following steps would be most important for establishing a valid conclusion?
Answer: Measuring the heart rate of the same daphnia in a drop of water without caffeine.
When you see a question about experimental design, focus on the fundamental requirement for any valid scientific experiment: having a proper control group for comparison. Without a control, you can't determine if your treatment actually caused the observed effect.
The correct approach here is D) Measuring the heart rate of the same daphnia in a drop of water without caffeine. This creates a baseline measurement from the same organism under identical conditions, except for the presence of caffeine. By comparing the heart rate with caffeine to the heart rate without caffeine, you can determine whether caffeine actually caused any change. This is called a control treatment, and it's essential for drawing valid conclusions about cause and effect.
A is incorrect because while controlling temperature is good experimental practice, it doesn't address the fundamental flaw—lack of a comparison baseline. You could maintain perfect temperature but still have no way to know if caffeine caused any heart rate changes.
B is wrong because changing to a different species would alter the experimental subject entirely, making it impossible to test the original hypothesis about daphnia specifically.
C is incorrect because testing different caffeine concentrations would be useful for understanding dose-response relationships, but without any control measurement (no caffeine), you still couldn't establish whether caffeine has any effect at all.
Remember this pattern: Valid experiments require controls. When you see experimental design questions on the GED, always look for the answer choice that provides an appropriate comparison group—typically the same conditions minus the variable being tested.
- Which change would most improve the reliability of his results?
Answer: Repeating the procedure several times for each brand and calculating an average.
When you encounter questions about experimental design, focus on what makes scientific results trustworthy and reproducible. The key issue here is reliability - how consistent and dependable the experimental results are.
The correct answer is D because repeating the procedure multiple times for each brand and calculating averages directly addresses the fundamental problem with this experiment: it was only performed once. Single trials are unreliable because they can't account for random variations, measurement errors, or unusual circumstances that might affect one particular test. By conducting multiple trials and averaging the results, you minimize the impact of these random factors and get a much more accurate picture of each paper towel's true absorbency.
Looking at why the other options miss the mark: A is incorrect because changing the liquid doesn't improve reliability - it changes what you're testing entirely. While testing with different liquids might be interesting for other research questions, it doesn't make the current water absorbency results more reliable. B is wrong because having a friend repeat the experiment once still gives you only single trials per brand, which doesn't solve the reliability problem. C is incorrect because changing the pan depth alters the experimental conditions rather than improving reliability, and could actually introduce new variables.
Remember this pattern for GED science questions: when you see "reliability," "accuracy," or "improve results," look for the answer choice that involves multiple trials or repeated measurements. Single trials are almost never sufficient for reliable scientific conclusions.
- What is the independent variable in this experiment?
Answer: The amount of acid added.
When you encounter an experiment question, you need to identify what the researcher is deliberately changing versus what they're measuring as a result. The independent variable is what the experimenter controls and manipulates on purpose.
In this experiment, the student is systematically changing one thing: the amount of acid added to each beaker (0 mL, 5 mL, 10 mL, and 15 mL). This is what the researcher controls and varies intentionally to see what effect it has. The correct answer is D because the amount of acid added is being deliberately manipulated.
Let's see why the other choices don't work: A is incorrect because the final pH is what's being measured as a response—this is the dependent variable, not the independent variable. The pH changes as a result of adding different amounts of acid. B is wrong because the type of acid stays the same throughout the experiment; the student uses the same strong acid in all trials, so this isn't being varied. C is incorrect because the initial volume of water remains constant at 100 mL in each beaker—this is a controlled variable that the researcher keeps the same to ensure a fair test.
Remember this key distinction: the independent variable is what you change on purpose (the input), while the dependent variable is what you measure as a result (the output). Look for what's being systematically varied across the different trials of the experiment.
- Why is it difficult to attribute the team's improvement solely to the new training regimen?
Answer: The improved weather conditions are a confounding variable that could affect times.
When evaluating scientific experiments, you need to identify confounding variables—factors other than the independent variable that could influence the results. This is crucial for determining whether observed changes are truly due to the treatment being tested.
The coach's study has a significant design flaw: she changed the training regimen at the same time that weather conditions improved. Since warmer, less windy conditions naturally make running faster (reduced air resistance and better muscle function in warmth), it's impossible to know whether the improved times came from the new training regimen, the better weather, or both. This makes weather a confounding variable that undermines the study's validity.
Looking at the incorrect options: Choice A about stopwatch precision is irrelevant—the issue isn't measurement accuracy but experimental design. Choice B suggesting testing a longer distance misses the point entirely; the problem isn't about which event to measure but about controlling variables. Choice C claims one month isn't long enough, but this contradicts the fact that improvement was actually observed—the issue is determining what caused that improvement.
The correct answer is D because the improved weather conditions represent a classic confounding variable that makes it impossible to isolate the effect of the training regimen.
For GED Science questions about experiments, always look for confounding variables—factors that change along with the treatment and could alternative explain the results. Well-designed experiments control these variables by keeping everything else constant except what's being tested.
- Which is a major flaw in this experimental design that makes the conclusion unreliable?
Answer: Using two different mice introduces individual variation as a confounding factor.
When evaluating scientific experiments, you need to identify whether the experimental design allows for valid conclusions. The key principle is that a good experiment controls for variables so that only the factor being tested (the independent variable) differs between groups.
This experiment has a critical flaw: it uses two different individual mice, which means any difference in maze completion time could be due to natural variation between the animals rather than the effect of Mozart. One mouse might simply be faster, smarter, or more motivated than the other, regardless of the music. To draw a reliable conclusion about Mozart's effect, you'd need either the same mouse tested under both conditions, or large groups of mice with some hearing Mozart and others running in silence.
Looking at the wrong answers: Choice A misses the point entirely - the specific composer isn't the experimental flaw, and Mozart is a reasonable choice for classical music. Choice C suggests the maze complexity is the problem, but a simple maze can still show valid differences if the experiment is properly designed. Choice D incorrectly assumes the animal choice matters for the experimental validity - mice are appropriate subjects for this type of behavioral study.
The correct answer is B because using two different mice introduces individual variation as a confounding factor that makes it impossible to determine whether any observed difference is due to the music or simply due to differences between the individual animals.
GED Science tip: When you see experimental design questions, always ask yourself: "What factors other than the one being tested could explain the results?" Look for uncontrolled variables that could confound the conclusion.
- What is the independent variable in this observational study?
Answer: The location in the river, representing different stream velocities.
When you encounter questions about scientific studies, you need to identify the variables correctly. The independent variable is what the researcher deliberately chooses or controls to study its effect, while the dependent variable is what gets measured as a result.
In this study, the geologist is investigating how stream velocity affects sediment size. She strategically chose three locations with different water speeds: a slow pool, a moderately fast section, and rapids. The location represents different stream velocities, making this her independent variable. She then measured the particle sizes at each location to see the effect.
Choice B is correct because the geologist selected these specific locations to represent different stream velocities - this is what she's manipulating or controlling in her study design.
Choice A represents the dependent variable, not the independent variable. The average particle size is what she's measuring to see how it responds to different stream velocities. Choice C (river depth) might vary between locations, but depth isn't what the geologist is systematically studying - she's focused on velocity's effect on sediment. The depth is just an incidental characteristic that isn't being investigated. Choice D (total water volume) also isn't the focus of this study and wouldn't necessarily be different at the three sampling locations within the same river.
Remember this pattern: the independent variable answers "What is the researcher changing or comparing?" while the dependent variable answers "What is being measured?" Look for what's being deliberately varied to test an effect.
- Why is it difficult to conclude that one fertilizer is better than the other based on this experiment?
Answer: Differences in soil type and sunlight are confounding variables.
When you encounter questions about experimental design, focus on whether the experiment can isolate the variable being tested. A good experiment should control for other factors that might influence the results.
This experiment has a fatal flaw: it's impossible to tell whether differences in corn yield come from the fertilizers or from other varying conditions. The north and south fields differ in two important ways beyond just the fertilizer type. The north field has loamy soil and more sunlight, while the south field has clay soil and less sunlight. These are confounding variables—factors other than the fertilizer that could affect corn growth. If the north field produces more corn, we can't determine whether it's because Fertilizer A is better, or because loamy soil drains better than clay, or because the extra sunlight boosted growth.
Choice A is wrong because testing a third fertilizer wouldn't fix the fundamental design problem—you'd still have uncontrolled variables affecting your results. Choice B misses the point; standardized measurements are important, but the passage doesn't suggest measurement was the issue. Choice C is incorrect because agricultural experiments are perfectly valid when done in field conditions—in fact, they're often more realistic than lab studies.
For GED Science questions about experiments, always ask yourself: "What else besides the tested variable could explain these results?" If you can identify other factors that vary between test groups, the experiment likely has confounding variables that make conclusions unreliable.
- What is a potential weakness in the method used to measure the rate of photosynthesis?
Answer: The oxygen bubbles may vary in size, making counting them an inaccurate measure.
When evaluating experimental design, you need to identify methodological flaws that could compromise data accuracy and reliability. This question tests your ability to spot measurement problems in scientific procedures.
The key issue here is that counting oxygen bubbles assumes all bubbles contain the same amount of oxygen, but bubble size can vary significantly based on factors like water temperature, pressure, and the physical structure of the plant's leaves. A few large bubbles might represent the same oxygen production as many small bubbles, making the counting method unreliable for measuring actual photosynthesis rates. Choice C correctly identifies this fundamental measurement flaw.
Let's examine why the other options miss the mark. Choice A suggests varying light intensity instead of color, but the researcher's hypothesis specifically focuses on color effects, making this change irrelevant to the research question. Choice B proposes adding a white light control group, which could be useful for comparison but isn't a weakness in the measurement method itself—it's more about experimental completeness. Choice D claims 10 minutes is too short, but this timeframe is actually reasonable for observing photosynthesis, and duration doesn't address measurement accuracy.
When you encounter experimental design questions on the GED, focus on whether the measurement method actually captures what the researcher claims to be measuring. Look for situations where the measurement technique has built-in assumptions that might not hold true—like assuming all bubbles are equal in this case.
- What is the independent variable in this experiment?
Answer: The type of de-icing agent used.
When you encounter an experiment question on the GED Science exam, you need to identify what the researcher is deliberately changing versus what they're measuring as a result. The independent variable is what the experimenter controls and manipulates, while the dependent variable is what gets measured in response.
In this de-icing experiment, the engineer is systematically changing one factor: the type of de-icing agent applied to each section. She deliberately chooses to use sodium chloride on one section, calcium chloride on another, magnesium chloride on a third, and leaves one untreated as a control. This is what she's manipulating to test its effect, making D the correct answer.
Let's examine why the other choices are incorrect. Choice A (time for ice to melt) is actually the dependent variable – this is what the engineer measures as a result of using different de-icing agents. Choice B (outside air temperature) isn't mentioned as something being controlled or varied; it's likely held constant throughout the experiment. Choice C (initial thickness of ice) is also held constant – the passage states the puddle was "evenly frozen" and divided into "equal sections," indicating this factor was controlled, not manipulated.
Remember this key distinction: the independent variable is what you change on purpose, and the dependent variable is what you measure to see the effect. Look for words like "different types," "various amounts," or "treated with" to identify what's being deliberately varied in the experiment.
- Why is this conclusion potentially flawed based on the observation alone?
Answer: It assumes causation from a correlation without ruling out other factors.
This question tests your understanding of a fundamental principle in scientific reasoning: the difference between correlation and causation. When you see two things occurring together, it's tempting to assume one causes the other, but good scientific thinking requires more careful analysis.
The scientist's conclusion is flawed because observing many owls and few mice at the same time doesn't prove that owls caused the low mouse population. This is a classic correlation-versus-causation error. The scientist assumes causation from correlation without considering alternative explanations or controlling for other variables. Answer B correctly identifies this logical flaw.
Let's examine why the other options miss the main issue. Answer A suggests the sample size is too small, but even studying multiple forests wouldn't fix the fundamental reasoning error if the scientist still assumed causation from observation alone. Answer C focuses on not counting other predators, which is a specific oversight but not the core logical problem. Answer D mentions disease as a possible cause, which is actually an example of the type of alternative explanation the scientist should consider, but it's too specific to be the best answer about why the reasoning is flawed.
The key insight is that many factors could explain a small mouse population: disease, food scarcity, habitat changes, or even that owls moved to this forest because mice were already scarce elsewhere.
On GED Science questions about scientific conclusions, always ask yourself: "Does this reasoning jump from 'these things happen together' to 'one causes the other' without ruling out other explanations?" This type of logical error appears frequently on the exam.
