Introduction: The "So What?" Phase

You have designed your experiment, collected your data, and drawn a beautiful graph. But how do you know if you can actually trust your results? This final stage of a scientific investigation is like being a detective. You look back at your work to see what went well, what went wrong, and how you could do it better next time. In the IB MYP, this falls under Criterion C: Processing and Evaluating.

Don't worry if this seems a bit abstract at first! Evaluating isn't about admitting you "failed"; it's about showing you understand the scientific process well enough to spot its strengths and weaknesses.

Evaluating the Validity of a Hypothesis (Strand C.iii)

After you have your results, you must look back at your original hypothesis (your "educated guess"). This is where you decide if your hypothesis was supported or not.

How to do it:

1. Check the trend: Does your graph show what you predicted? If you said, "If I increase the temperature, the sugar will dissolve faster," and your graph shows the time decreasing as temperature goes up, then your hypothesis is valid.
2. Use your data: Don't just say "it worked." Use numbers! For example: "The hypothesis was supported because at \(20^{\circ}C\) the sugar took 60 seconds to dissolve, while at \(40^{\circ}C\) it only took 30 seconds."
3. Be honest: If your data contradicts your hypothesis, that is okay! In science, "disproving" something is just as important as "proving" it. You would state that the hypothesis is invalid based on the evidence collected.

Quick Review: A "valid" hypothesis in this context simply means your data backs up your claim. If the data is messy or doesn't show a clear pattern, the validity of your conclusion might be low.

Evaluating the Validity of the Method (Strand C.iv)

This is where you judge the procedure you followed. A valid method is one that truly measures what it is supposed to measure. It is often called a "fair test."

Ask yourself these questions:

  • Were the variables controlled? If you were testing how light affects plants but forgot to give them the same amount of water, your method has low validity.
  • Was the equipment appropriate? If you tried to measure \(1.5 ml\) of liquid using a large \(100 ml\) beaker, your measurements weren't very precise.
  • Was there "noise" or "interference"? Did the room temperature change during the day? Was the scale flickering?

The Two Big Error Types:

1. Random Errors: These are "one-off" mistakes, like misreading a stopwatch or a slight breeze hitting a scale. We reduce these by doing repeats and calculating an average (mean).
2. Systematic Errors: These are "built-in" mistakes. For example, if your weighing scale starts at \(0.5 g\) instead of \(0.0 g\), every single result will be wrong by the same amount. These are much harder to fix after the experiment is done!

Did you know? Even the best scientists in the world deal with these errors. The goal isn't to be perfect, but to identify where the errors came from.

Explaining Improvements (Strand C.v)

Once you have identified the weaknesses in your method, you need to suggest improvements. An improvement is a specific change that would make your current experiment more accurate or valid.

How to write a great improvement:

Don't just say "be more careful" or "do it again." Be specific!

  • Instead of: "Use better equipment."
  • Try: "Use a digital thermometer with a precision of \(\pm 0.1^{\circ}C\) instead of a liquid thermometer to reduce reading errors."

  • Instead of: "Control the variables better."
  • Try: "Use a water bath to keep the temperature of the test tubes constant at \(30^{\circ}C\) throughout the experiment."

Memory Aid: Think S.E.C. — Source of error (what was wrong), Effect (how it messed up the data), and Change (how to fix it).

Explaining Extensions (Strand C.v)

An extension is different from an improvement. While an improvement fixes the experiment you just did, an extension looks at "what's next?" It asks how you could expand the investigation to learn even more.

Ways to extend an investigation:

  1. Change the range: If you tested temperatures from \(20^{\circ}C\) to \(50^{\circ}C\), you could extend it to \(80^{\circ}C\) to see if the trend continues.
  2. Change the Independent Variable: If you tested how amount of fertilizer affects plant growth, you could extend the experiment by testing different types of fertilizer.
  3. Apply to a new context: If you tested how friction works on wood, you could extend it to see how it works on ice or rubber.

Key Takeaway: Improvements make the current data better; extensions make the research broader.

Summary Checklist for Success

When you are writing your evaluation for a lab report or an eAssessment, check that you have:

  • Hypothesis: Stated if it was supported or not using data points.
  • Method: Discussed if it was a fair test (control variables) and if the equipment was precise.
  • Errors: Identified at least one random or systematic error.
  • Improvements: Suggested specific equipment or procedural changes to fix those errors.
  • Extensions: Suggested a new related question or a wider range to test.

Top Tip: Use "scientific reasoning" in your evaluation. If you suggest an improvement, explain why it would make the data more reliable. For example: "Using a pipette would increase the validity because it allows for a more precise volume measurement than a measuring cylinder, reducing the percentage error."