Evaluate a Hypothesis or Model
Sample Question:
Do the results of Experiment 1 support the hypothesis that increasing temperature increases solubility?
What’s Being Tested: Can you determine whether the data agrees with a stated model or hypothesis?
Knowledge & Skills Required:
- Understanding of experimental trends
- Comparing empirical results to theoretical claims
- Recognizing supporting vs. contradicting outcomes
What’s Needed to Answer Correctly:
- Ability to match a trend in the data with the direction and scope of the hypothesis
- Avoiding partial matches or irrelevant data
Correct Approach:
- Identify the core of the hypothesis (e.g., solubility ↑ with temp)
- Locate the relevant data or graph showing solubility vs. temperature
- Judge whether the observed pattern clearly supports, contradicts, or has no relation to the hypothesis
Determine Whether a Result Supports, Contradicts, or Is Inconclusive
Sample Question:
Does the outcome of Trial 3 contradict the general trend observed in Trials 1 and 2?
What’s Being Tested: Can you spot an outlier or exception and interpret whether it breaks the pattern?
Knowledge & Skills Required:
- Trend recognition across multiple trials
- Distinguishing between an outlier vs. expected variation
What’s Needed to Answer Correctly:
- Knowing the difference between support, contradiction, and inconclusiveness
- Recognizing that one inconsistent result may not fully disprove a model
Correct Approach:
- Compare the direction and magnitude of Trial 3’s result to the others
- Consider whether it clearly violates the established trend
- Determine if the deviation is large enough to be called a contradiction
Apply a Model to a New Situation
Sample Question:
If the model is correct, what should happen to the reaction rate when the acid concentration is increased to 2.0 M?
What’s Being Tested: Can you use the given model or trend to make a prediction in a new condition?
Knowledge & Skills Required:
- Understanding how to extend a trend (e.g., doubling concentration affects rate)
- Applying cause-and-effect reasoning to a hypothetical condition
What’s Needed to Answer Correctly:
- Careful application of existing patterns without over- or under-generalizing
- Avoid inserting outside chemical knowledge unless the model justifies it
Correct Approach:
- Determine how the dependent variable (e.g., rate) responded to earlier concentration changes
- Apply the same direction and rough magnitude of effect to the new condition
- Eliminate options that contradict the model’s trend
Predict a Result Based on a Given Model or Explanation
Sample Question:
If an unknown compound is similar to Substance A, how is it expected to behave under the conditions of Experiment 2?
What’s Being Tested: Can you extrapolate behavior or outcomes for a new scenario using prior information?
Knowledge & Skills Required:
- Pattern generalization
- Categorizing new substances or inputs based on similarity to known ones
What’s Needed to Answer Correctly:
- Use model or pattern from the original data to generate a valid prediction
- Ensure that similarity in properties (e.g., structure, polarity) justifies the comparison
Correct Approach:
- Identify how Substance A behaved under those conditions
- Match the unknown’s relevant features to A’s
- Choose the outcome consistent with A’s observed behavior
Distinguish Between Competing Explanations
Sample Question:
Based on the data, which explanation best accounts for why the pH dropped in Trial 4?
What’s Being Tested: Can you determine which of multiple plausible explanations is most consistent with the data?
Knowledge & Skills Required:
- Connecting cause-effect reasoning to outcomes
- Evaluating whether each proposed explanation matches the conditions and results
What’s Needed to Answer Correctly:
- Ability to test each explanation against known variables and outcomes
- Reject explanations inconsistent with the trial’s details
Correct Approach:
- Review the trial conditions (what was added, what changed)
- Evaluate whether each explanation logically connects those conditions to the outcome
- Select the one that best fits the full context
Identify a Flaw or Inconsistency in a Model or Conclusion
🧾Sample Question:
What aspect of the data contradicts the researcher’s claim that the reaction was complete at 40 seconds?
What’s Being Tested: Can you spot when the data disagrees with the stated conclusion or interpretation?
Knowledge & Skills Required:
- Understanding of what constitutes a completed reaction (e.g., plateau in volume or color)
- Comparing graphical or tabular data to a claim
What’s Needed to Answer Correctly:
- Ability to read data precisely (e.g., if a reaction continues changing after 40s, it’s not complete)
- Understanding when a claim overextends the evidence
Correct Approach:
- Locate the time or condition mentioned in the claim
- Observe whether any change still occurs beyond that point
- Identify any clear deviation between the claim and the actual trend
Reconcile a Discrepant Result with a Model
Sample Question:
Trial 5 produced a much slower reaction rate than expected. Which of the following might explain this result without disproving the model?
What’s Being Tested: Can you explain an exception as a special case — not as a full contradiction?
Knowledge & Skills Required:
- Understanding of real-world factors that might interfere (e.g., contamination, measurement error)
- Differentiating between a flawed model and a flawed execution
What’s Needed to Answer Correctly:
- Choose the explanation that preserves the model’s integrity while accounting for the anomaly
- Reject options that require discarding the model entirely
Correct Approach:
- Consider setup differences or external factors that might affect Trial 5 only
- Look for explanations that isolate the issue to that trial (e.g., lower temp, timing error)
- Select the choice that reconciles the data with the broader pattern