- Two hypotheses explain the increase in jellyfish populations. Hypothesis 1 states warmer ocean temperatures increase jellyfish numbers. Hypothesis 2 suggests overfishing of predators is the cause. Data correlates higher jellyfish numbers with warmer temperatures. Which hypothesis is more supported?
Answer: Hypothesis 1 is more supported.
Hypothesis 1 is more supported because it correctly predicts that warmer ocean temperatures increase jellyfish populations. The data correlates higher jellyfish numbers with warmer temperatures, which directly aligns with Hypothesis 1's temperature-driven population growth prediction. Hypothesis 2 suggests overfishing of predators as the cause, but this explanation doesn't account for the observed temperature-population correlation patterns. The evidence clearly demonstrates that thermal conditions, not predator removal alone, drive the observed jellyfish population increases.
- Based on Figure 1, which model better describes the rocket's flight?
Answer: Model 2, because it stays within a few metres of every measurement
Model 2 sits within about 1 m of every measured altitude across the whole flight, while Model 1 climbs away from the measurements and is 18 m too high by 5 seconds. Agreement across the measurements is what makes Model 2 the better description. Predicting a greater or a lower altitude is not by itself a reason to prefer a model, and the two models really are close at 1 second, which is why neither of those facts settles it.
- Which model tracks the young trees closely and then departs the most from the measurements as the trees age?
Answer: Model 3
Model 3 is within 0.1 m at age 5 and 0.5 m at age 10, closer to the young trees than Model 2 manages anywhere, and then keeps climbing while the real trees level off: it misses by 2.0 m at age 15 and 4.5 m at age 20, about 7.1 m in total. Model 2 has the opposite shape, running 1.1 m low at age 5 and closing to 0.5 m by age 20, about 4.6 m in total. Model 1 stays within 0.1 m throughout. Only Model 3 starts well and ends badly.
- A city measures why ground-level ozone (O3) peaks on some summer days. Two scientists propose different explanations. Scientist A (Sunlight-Driven Formation): Ozone forms faster when sunlight is stronger. Prediction: For similar traffic emissions, O3 should rise with increasing solar radiation. Scientist B (Stagnation/Accumulation): Ozone peaks mainly when winds are weak, allowing pollutants to accumulate. Prediction: For similar sunlight, O3 should be higher on low-wind days. Measurements were taken on four days with similar traffic counts. Figure 1 shows O3 concentration at 4 PM versus wind speed, with points labeled by solar radiation category (Low vs High). Which explanation most logically accounts for the results in Figure 1?
Answer: Both scientists, because O3 increases with higher solar radiation and also decreases as wind speed increases.
Both Scientist A's and Scientist B's explanations are supported by the results, as ozone levels respond to both solar radiation and wind speed. Figure 1 shows that for similar wind speeds, O3 is higher on high solar days (110 ppb at 1 m/s vs. 70 ppb, and 60 ppb at 5 m/s vs. 35 ppb), while for the same solar category, O3 decreases with increasing wind speed (from 110 to 60 ppb in high solar, and 70 to 35 ppb in low solar). This aligns with Scientist A's prediction of higher O3 under stronger sunlight and Scientist B's prediction of accumulation on low-wind days, demonstrating independent effects of each factor. The data contradicts neither scientist alone, as both trends are evident without reliance on unmeasured variables like traffic. - Researchers propose two theories for the development of antibiotic resistance. Theory 1 states it arises from natural selection. Theory 2 suggests horizontal gene transfer as the cause. Genetic studies show resistance genes spreading via gene transfer. Which theory is more consistent with genetic evidence?
Answer: Theory 2 is more consistent with the evidence.
Theory 2 is more consistent with the genetic evidence because it correctly identifies horizontal gene transfer as the mechanism for resistance spread. The genetic studies show resistance genes spreading via gene transfer, which directly supports Theory 2's prediction about lateral genetic exchange. Theory 1 suggests natural selection alone, but this mechanism doesn't explain the observed rapid gene transfer patterns between different bacterial populations. The evidence clearly demonstrates that genetic exchange, not just selective pressure, drives resistance dissemination.
- Which of the following is the strongest reason to doubt that prediction?
Answer: A 40 N load is far outside the range of loads that were tested.
The model was fitted to loads from 2 N to 10 N, and 40 N is four times the largest load ever placed on the spring. Nothing in Figure 1 shows whether the spring keeps stretching in proportion that far, and a spring stretched far enough stops obeying a straight-line rule altogether. The model does pass through every measured point, which is why the other objections fail.
- Scientists propose two mechanisms for the development of hurricanes. Mechanism 1 suggests warm ocean waters are key. Mechanism 2 claims atmospheric conditions are more important. Data shows hurricanes forming over warm waters. Which mechanism is better supported by the data?
Answer: Mechanism 1 is better supported by the data.
Mechanism 1 is better supported by the data because it correctly identifies warm ocean waters as key to hurricane development. The data shows hurricanes forming over warm waters, which directly validates Mechanism 1's prediction about thermal energy requirements. Mechanism 2 claims atmospheric conditions are more important, but this explanation doesn't account for the observed strong association between warm sea surface temperatures and hurricane formation locations. The evidence clearly demonstrates that ocean thermal energy, not just atmospheric factors, drives hurricane genesis.
- Up to which charging time does the model match the measurements?
Answer: 40 min
The model and the measurements agree exactly at 10, 20, 30, and 40 minutes. At 50 minutes the model predicts 60% against a measured 56%, and at 60 minutes it predicts 72% against a measured 60%. Charging slows as the battery fills, which the model does not capture, so the model holds up to 40 minutes.
- Which model is better supported by the measurements in Figure 1?
Answer: Model 1, because its predictions match the measurements at every time shown
Model 1 predicts 2, 4, 8, 16, and 32 g, matching every measured point in Figure 1. Model 2 is right at day 0 but too high at days 2, 4, and 6, and too low at day 8. A model is supported by agreeing with the measurements, not by predicting a larger or smaller number.
- Two models predict the effects of deforestation. Model 1 suggests increased carbon emissions. Model 2 claims it leads to biodiversity loss. Data shows higher carbon emissions following deforestation. Which model is best supported by the data?
Answer: Model 1 is best supported by the data.
Model 1 is best supported by the data because it correctly predicts increased carbon emissions from deforestation. The data shows higher carbon emissions following deforestation, which directly validates Model 1's prediction about forest carbon loss. Model 2 claims deforestation leads to biodiversity loss, which may also be true but doesn't explain the specific carbon emission data presented. The evidence clearly demonstrates that deforestation increases atmospheric carbon, supporting Model 1's carbon-focused prediction over Model 2's biodiversity emphasis.
- At which true temperature does the model overstate the thermistor's reading by the most?
Answer: 80 °C
The model predicts the reported temperature will equal the true temperature. The thermistor reads 0.5 °C high at 0 °C and low everywhere above that: 1 °C low at 20 °C, 3 °C low at 40 °C, 6 °C low at 60 °C, and 10 °C low at 80 °C. The model therefore overstates the reading by the most at 80 °C, and the shortfall grows as the temperature rises.
- Two hypotheses are proposed to explain the increase in atmospheric CO2. Hypothesis 1 states that fossil fuel combustion is the main cause. Hypothesis 2 suggests that deforestation is the primary factor. Data shows a strong link between fossil fuel use and CO2 levels. Which hypothesis is better supported?
Answer: Hypothesis 1 is better supported.
Hypothesis 1 is better supported because it correctly identifies fossil fuel combustion as the main driver of atmospheric CO2 increase. The data shows a strong link between fossil fuel use and CO2 levels, which directly validates Hypothesis 1's prediction about combustion emissions. Hypothesis 2 suggests deforestation as the primary factor, but this explanation doesn't account for the observed strong correlation between fossil fuel consumption and atmospheric CO2 concentrations. The evidence clearly demonstrates that energy-related emissions, not land use changes alone, drive the observed CO2 trends.
- Two scientists propose different mechanisms for the sudden extinction of dinosaurs. Scientist A suggests a massive asteroid impact, while Scientist B points to gradual volcanic activity. Fossil records show a sudden extinction event. Which hypothesis is more consistent with the fossil evidence?
Answer: Scientist A's hypothesis is more consistent.
Scientist A's hypothesis is more consistent with the fossil evidence because it correctly predicts sudden extinction events. The fossil records show a sudden extinction event, which aligns with Scientist A's asteroid impact hypothesis that would cause rapid, catastrophic changes. Scientist B's gradual volcanic activity hypothesis predicts gradual extinction patterns that contradict the observed sudden disappearance in the fossil record. The evidence specifically supports catastrophic rather than gradual extinction mechanisms.
- Researchers present two models for the cause of increased forest fires. Model 1 suggests climate change increases fire frequency. Model 2 attributes it to human activity. Data shows more fires in regions with higher temperatures. Which model is best supported by the data?
Answer: Model 1 is best supported by the data.
Model 1 is best supported by the data because it correctly predicts that climate change increases fire frequency through higher temperatures. The data shows more fires in regions with higher temperatures, which directly validates Model 1's climate-fire relationship. Model 2 attributes fires to human activity, but this explanation doesn't account for the observed temperature-fire correlation across regions. The evidence clearly demonstrates that temperature increases, consistent with climate change, drive the observed fire frequency patterns.
- Which result for the fifth sample would show the model to be wrong?
Answer: The 50 µm sample loses 19 mg.
The model says a thicker coating never loses more than a thinner one, so the 50 µm sample must lose no more than the 40 µm sample's 15 mg. Losing 19 mg is more than the thinner coating lost, which the model says cannot happen. Losing 8 mg or 12 mg fits the model, and so does losing exactly 15 mg, since the model rules out only an increase.
- Students test why an enzyme's reaction rate decreases at very high substrate concentrations. Two models are proposed.
Model 1 (Saturation Only): Rate approaches a maximum as substrate increases, then stays near that maximum. Prediction: At very high substrate, rate should plateau, not decrease.
Model 2 (Substrate Inhibition): Excess substrate binds to an inhibitory site, reducing activity. Prediction: Rate should increase at first, then decrease at very high substrate.
Figure 1 shows reaction rate versus substrate concentration.
Which model is best supported by the results in Figure 1?
Answer: Model 2, because the rate decreases at very high substrate after reaching a peak at intermediate substrate.
Model 2, the Substrate Inhibition Model, is best supported by the results in Figure 1. The figure depicts reaction rate increasing from 0 to 80 units between 0–5 mM substrate, peaking at 90 units at 10 mM, and then decreasing to 60 units at 40 mM. This pattern aligns with Model 2's prediction of an initial rise followed by a decline at high substrate due to inhibitory binding, showing a non-plateauing response. Model 1 is contradicted because the rate does not plateau but instead decreases after the maximum, inconsistent with saturation-only expectations.
- A study examines the impact of sunlight on plant growth. Model 1 suggests plants grow faster with more sunlight due to increased photosynthesis. Model 2 suggests growth is limited by nutrient availability, not sunlight. Data shows plants exposed to more sunlight grew faster. Which model is best supported by the data?
Answer: Model 1 is best supported; sunlight increases growth.
Model 1 is best supported by the data because it correctly predicts that increased sunlight leads to faster plant growth. The experimental data shows plants exposed to more sunlight grew faster, which directly aligns with Model 1's prediction that sunlight increases growth through enhanced photosynthesis. Model 2 suggests growth is limited by nutrients rather than sunlight, but this prediction is contradicted by the observed relationship between sunlight exposure and growth rate. The data clearly demonstrates that sunlight, not nutrient limitation, was the controlling factor in this study.
- Two scientists debate the primary cause of climate change. Scientist A argues carbon emissions are the main factor, while Scientist B suggests solar activity plays a larger role. Data shows a strong correlation between carbon levels and temperature rise. Which explanation most logically accounts for the results?
Answer: Scientist A's explanation is most logical.
Scientist A's explanation is most logical because it correctly identifies carbon emissions as the primary climate change driver. The data shows a strong correlation between carbon levels and temperature rise, which directly supports Scientist A's argument that carbon emissions are the main factor. Scientist B's emphasis on solar activity cannot account for the observed correlation between atmospheric carbon concentrations and global temperature increases. The evidence clearly demonstrates that carbon emissions, not solar variations, explain the documented temperature trends.
- Two models predict the behavior of gases under pressure. Model 1 states that pressure increases proportionally with temperature. Model 2 claims that pressure is constant regardless of temperature. Experimental data shows pressure increasing with temperature. Which model is supported by the data?
Answer: Model 1 is supported by the data.
Model 1 is supported by the data because it correctly predicts that pressure increases proportionally with temperature. The experimental data shows pressure increasing with temperature, which directly validates Model 1's prediction of proportional relationships. Model 2 claims pressure is constant regardless of temperature, which is directly contradicted by the observed temperature-pressure correlation. The evidence clearly demonstrates a temperature-dependent pressure relationship rather than temperature independence.
- Two scientists propose explanations for the rapid melting of polar ice. Scientist A suggests global warming is the primary factor, while Scientist B claims ocean currents are more influential. Temperature data correlates with ice melt rates. Which explanation most logically accounts for the results?
Answer: Scientist A's explanation is most logical.
Scientist A's explanation is most logical because it correctly identifies global warming as the primary factor in polar ice melting. The temperature data correlates with ice melt rates, which directly supports Scientist A's argument that global warming drives ice loss. Scientist B's emphasis on ocean currents cannot fully account for the observed temperature-melt correlation across different regions and time periods. The evidence clearly demonstrates that atmospheric warming, not just ocean circulation changes, explains the documented ice melt patterns.
