Asked by kj

Morgan is conducting an experiment to determine which type of container traps the most heat. Morgan hypothesizes that Aluminum Foil will trap the most heat. Below are some portions of her experiment.

Materials

3 shoe boxes
Timer
Aluminum Foil
Plastic Wrap
Wax Paper
3 Thermometers
Procedures

Step 1. Gather your materials.

Step 2. Create a hole in each box for the thermometer.

Step 3. Set the timer for 10 minutes and put the Aluminum Foil on one box, Wax Paper on the other, and Plastic Wrap on the third.

Step 4. Put the shoe boxes in the sun and start the timer.

Step 5. When the timer goes off record the temperature in each box.

Step 6. Remove coverings and allow each box to cool to room temperature.

Step 7. Repeat steps 3-6 twice for a total of 3 trials.



Temperature Data Table



Trial 1

Trial 2

Trial 3

Average Temperature

Aluminum Foil

101OF

102 OF

101 OF

101.3 OF

Plastic Wrap

100 OF

101 OF

100 OF

100.3 OF

Wax Paper

99 OF

99 OF

100 OF

99.3 OF

Answers

There are no human answers yet.
Answered by GPT-5 mini
Summary of results
- Averages (from your table)
- Aluminum foil: 101.3 °F (trials: 101, 102, 101)
- Plastic wrap: 100.3 °F (100, 101, 100)
- Wax paper: 99.3 °F (99, 99, 100)

- Sample standard deviations (n = 3)
- All three coverings have the same SD ≈ 0.58 °F.

Short conclusion
- Morgan’s hypothesis (aluminum foil traps the most heat) is weakly supported: aluminum had the highest average temperature (about 1.0 °F higher than plastic wrap and 2.0 °F higher than wax paper). However, the differences are small compared with the measurement variability and the experiment’s limitations, so the result is not strong or definitive.

Variables (clear statement)
- Independent variable: type of covering (aluminum foil, plastic wrap, wax paper).
- Dependent variable: temperature inside each box after 10 minutes.
- Controls that should be maintained: same boxes and thermometers, same location and exposure time, same orientation to the sun, same starting (ambient) temperature.

Possible sources of error / limitations
- Small sample size (only 3 trials) → low statistical power.
- Short exposure time (10 minutes) may produce small temperature differences.
- Thermometer calibration or reading error (analog reading error ±1°F can be important here).
- Sun intensity changed between trials or during the 10 minutes (clouds, angle of sun).
- Box positions may not have received identical sunlight (shadowing or reflections).
- Holes for thermometers may have let hot air escape or let cooler air in; coverings may not have been sealed equally.
- Starting temperatures for each trial may not have been identical.
- Shoe boxes may have different interiors or colors that affect absorption.

Ways to improve the experiment
- Increase the number of trials (e.g., at least 5–10) to reduce random error.
- Run longer exposures (e.g., 30–60 minutes) or record temperature continuously (every minute) to capture warming curves.
- Calibrate or use identical, accurate thermometers (digital or thermocouples).
- Measure and record ambient (starting) temperature each trial.
- Randomize or rotate box positions between trials so each covering experiences the same sun conditions.
- Seal the coverings consistently around the thermometer hole to prevent air leakage.
- Keep box interiors identical (same color/insulation) and control for reflectivity/absorption differences.
- Increase sample size by using more boxes per covering (replicate boxes simultaneously).
- If you want to test statistical significance, collect more data and use ANOVA or t-tests to compare means.

Suggested next steps to strengthen your conclusion
1. Repeat the experiment with more trials and better control of exposure conditions.
2. Plot the temperatures vs. time for each covering to see whether differences grow with time.
3. After collecting more data, run a simple ANOVA or pairwise t-tests to see whether the observed differences are statistically significant.

If you’d like, I can:
- provide a suggested revised procedure that includes the improvements above,
- create a template data table or graph example,
- or help compute a statistical test once you have more data.

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