2. What makes a good experiment?
Only 1 independent variable…but can have different values.
Ex: How does the amount of fertilizer effect the growth of plants? You could vary the amount of the same kind of fertilizer OR
Ex: Which fertilizer is the best? You would vary the type of fertilizer BUT use the same amount of fertilizer
For both of these examples there is only 1 independent variable…the fertilizer.
5 minute notes…

3. As many controlled (constant) variables as possible
As many controlled (constant) variables as possible. In the case of plants this could be: amount of water, amount of sun light, amount & type of soil, type of plant & size of plant….etc.
The more that is kept constant the more valid your data becomes because the change in your data is more likely due to the independent variable, not some other factor.
Trials…as many as logistically possible. Why? To reduce error!
What if the fertilizer was mixed wrong? You wouldn't know unless there was more then 1 trial to see the data that didn’t match the rest. You should see consistency in data for all trials or something is wrong.

4. Sample Size: as many as possible. Again to reduce error.
Only one plant per type of fertilizer? What if one plant dies because it doesn’t get watered due to human error? That would skew the results giving a possible incorrect conclusion. So, you need many subjects
Length of exp: how long will the study go on?
Data: how is it collected, measured, etc…