SAC 3 Flashcards
(31 cards)
What trend(s) can you see in your data?
As temperature increased from 2°C to 65°C, the rate of reaction also increased. The highest reaction rate was observed at 65°C.
● What relationship can you observe between your independent and dependent variable?
The independent variable (temperature) directly affected the dependent variable (oxygen concentration). Higher temperatures increased catalase activity, producing more oxygen
● Can you explain this using biological terminology (linking to enzyme activity)?
increased temperature raises kinetic energy, = more frequent collisions between catalase and hydrogen peroxide. = forms more enzyme-substrate complexes, = speeds up the breakdown into oxygen and water = enzyme denatures at too high a temperature.
● Does your data support your hypothesis?
Partially. Rate increased with temp as predicted, but didn’t decrease—suggesting 65°C wasn’t high enough to cause denaturation during the test time.
● Do you have any anomalous data or outliers?
The data appears consistent. No clear outliers were identified, but the reaction rate at 65°C being highest is unusual since many enzymes denature at that point.
● What limitations or uncertainties did you have in your experimental method?
- set measurement time may have been too short to capture full reaction = missing later changes in oxygen production
- inconsistent yeast ball size
- slight delays in sensor calibration could have affected accuracy. (oxygen sensor not fully calibrated when starting measurements)
● Are your results what you expected? Can you account for any unusual results?
Mostly yes, though the highest activity at 65°C may suggest this particular catalase tolerates heat well or wasn’t denatured within the timeframe.
● What sources of error may have been present? How did they affect your results?
Systematic error: If the enzyme or substrate is consistently too concentrated or diluted, it would affect enzyme activity in the same way for every trial, leading to systematic errors.
Random error: inconsistent water temperatures (eg 49.7 not 50 degrees, or 36.2 not 36)
● How could you improve your experimental method?
I could use more precise measuring tools, better timing (like a timer), and increase temperature control. I’d also repeat the experiment more times for better reliability.
● What implications are there from your findings? What further investigations could be undertaken?
Understanding enzyme activity helps in biotech, medicine, and food industries. further investigations could test temperature range for denaturation: Test a range of temperatures (e.g., 30°C, 40°C, 50°C) to find the optimal temperature for enzyme activity and the point at which denaturation begins.
● What is a real-life application of your finding
This experiment relates to how enzymes break down substances in the body—for example, catalase breaking down hydrogen peroxide in cells.
Accuracy
How close a measurement is to the true or accepted value.
Precision
How close a measurement is to the true or accepted value.
Repeatability
The ability to obtain the same results when the same person repeats the experiment using the same method and equipment
Reproducibility
The ability to get the same results when different people repeat the experiment using the same method.
Validity
Whether the experiment actually tests what it claims to
Reliability
How consistent and dependable the results are, often improved through repeated trials and clear procedures.
Controlled Experiment:
A controlled experiment tests one independent variable while keeping all other controlled variables constant.
Qualitative
Descriptive data (e.g., color changes); shown in bar graphs or pie charts.
Quantitative
Numerical data (e.g., reaction rate); shown in line graphs or histograms.
Variables:
Independent -
Dependent -
Controlled -
I: What you change.
D: What you measure.
C: What you keep the same.
Purpose of a Control Group
baseline for comparison. To compare results and see if the change in the independent variable actually causes a difference
role of systematic errors
- Caused by flaws in equipment or method.
- Affect all results in the same direction (e.g., always too high).
- Impact accuracy.
- Example: Miscalibrated oxygen sensor, incorrect solution concentrations.
role of random errors
- Caused by unpredictable changes in conditions.
- Vary in magnitude and direction between trials.
- Impact precision.
- Example: Slight temperature shifts in water bath, bubble interference during measurement.