ACT ACT Science Practice Test 2025

Looking at the Day 21 column, seedling height increases as light intensity increases from 500 lux to 2,000 lux (9.8 to 17.2 cm, a large jump). However, from 2,000 to 4,000 lux, the increase slows (17.2 to 18.1 cm), and from 4,000 to 8,000 lux, the increase is minimal (18.1 to 18.3 cm). This indicates that height increases with light intensity but the rate of increase levels off at higher intensities, which is characteristic of a pattern where growth increases at a decreasing rate.

From the table, the 2,000 lux group had an average height of 6.0 cm at Day 7 and 11.8 cm at Day 14.

The increase is: $11.8 - 6.0 = 5.8$ cm.

This is the largest week-over-week increase for this group across the three measurement periods (Day 0-7 saw about 6.0 cm of growth, Day 7-14 saw 5.8 cm, and Day 14-21 saw $17.2 - 11.8 = 5.4$ cm).

The data shows that growth is already leveling off significantly between 4,000 and 8,000 lux (18.1 cm vs. 18.3 cm, only a 0.2 cm difference when light intensity doubled). The trend indicates that the seedlings are approaching a maximum height that is not limited by light. Doubling the intensity again from 8,000 to 16,000 lux would likely produce very little additional growth. Therefore, the height would most likely be very close to 18.3 cm -- perhaps around 18.4 cm. Among the choices, 18.4 cm is the best prediction.

The independent variable is the factor that the experimenter deliberately changes. In Experiment 1, the students varied the concentration of HCl while keeping the temperature constant at 25°C, using the same volume (50 mL) and the same length of Mg ribbon (3 cm). Therefore, the independent variable is the concentration of HCl.

The dependent variable (what is measured as a result) is the time for the Mg to dissolve. Temperature, volume, and ribbon length are controlled variables.

In Experiment 1, increasing HCl concentration decreased the dissolve time (from 185 s at 0.5 M to 23 s at 4.0 M). In Experiment 2, increasing temperature also decreased the dissolve time (from 162 s at 10°C to 28 s at 55°C). Both factors -- higher concentration and higher temperature -- result in a faster reaction rate (shorter dissolve time).

Option (a) correctly captures both conclusions. Option (b) is incorrect because higher temperature speeds up the reaction. Option (c) is incorrect because both variables affect the rate, not just one. Option (d) is the reverse of what the data shows.

Looking at the trend in Experiment 2, the dissolve time decreases as temperature increases:

• 10°C: 162 s
• 25°C: 94 s
• 40°C: 51 s
• 55°C: 28 s

The pattern shows that the dissolve time roughly halves (or more) for each 15°C increase. From 40°C to 55°C the time went from 51 s to 28 s. Following this trend, at 70°C (another 15°C increase), the time should decrease further below 28 s. The most reasonable estimate among the choices would be approximately 16 seconds, which continues the roughly halving pattern.

Both scientists acknowledge that average summer temperatures in the region have been increasing over the past 30 years. This is the shared observational premise that both scientists are attempting to explain -- they differ only on the primary cause.

Scientist A attributes the warming primarily to greenhouse gases, while Scientist B attributes it primarily to atmospheric circulation shifts and urban heat islands. But neither disputes that the warming has occurred.

Option (c) is something only Scientist A would emphasize. Option (d) is something only Scientist B focuses on. Option (a) represents the common ground.

Scientist B argues that shifting wind patterns and urban heat islands -- local and regional factors -- are the primary drivers. If it were shown that rural areas far from cities in the same region experienced the same rate of warming as urban areas, this would undermine two pillars of Scientist B's argument: (1) it would suggest the urban heat island effect is not a significant contributor, and (2) it would imply a more widespread forcing mechanism (like GHGs) is responsible rather than localized factors.

Option (a) would actually support Scientist B's argument. Option (c) is irrelevant to distinguishing between the two hypotheses. Option (d) would support Scientist A, but does not directly undermine Scientist B's specific claims about wind patterns and heat islands the way option (b) does.

Scientist A argues that GHGs are the primary driver and specifically states that 'natural factors such as solar cycles and volcanic activity can influence temperature, their effects over this 30-year period are small compared to the GHG forcing.' This argument assumes that natural climate variability (solar cycles, volcanic activity, etc.) has not been a major contributor over this specific 30-year period. If natural factors were in fact dominant, then the correlation between rising CO$_2$ and rising temperatures could be coincidental rather than causal.

Option (a) is something Scientist B claims, not an assumption of Scientist A. Option (c) is too strong -- Scientist A acknowledges natural factors exist, just claims they are small. Option (d) is not assumed by Scientist A; Scientist A focuses on the atmosphere, not urban development.

Scientist B emphasizes local and regional factors (wind pattern shifts, urban heat islands) as the primary drivers. Scientist A cites climate models that incorporate GHG levels and reproduce the warming trend. Scientist B would most likely counter that these global-scale climate models do not adequately account for the regional-scale factors that Scientist B considers most important -- namely, the shifts in atmospheric circulation patterns and local urban heat island effects. If the models focus primarily on GHG forcing, they might reproduce the general warming trend for the wrong reasons, missing the actual regional mechanisms.

Option (a) attacks the existence of models, which is too extreme. Option (c) is irrelevant to Scientist B's specific argument. Option (d) goes against the premise that both scientists accept the warming trend.