The Experiment
:: Materials
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Why is it helpful to make a list of all the materials you need for your experiment? It helps you make sure you have everything you need when you need it. Be specific and include details like dimensions and weight if necessary. Approximate measurements might be okay for some projects.Here's a sample list of materials from our sunscreen lotion experiment.
Materials List
- 16 sheets of glass (approximately 550 mm in length x 550 mm wide).
- 5 brands of sunscreen with SPF 15
- 5 brands of sunscreen with SPF 30
- 5 brands of sunscreen with SPF 50
- 1 UV meter to measure the UV index readings
- 1 bottle of glass cleaner
- A box of disposable gloves (at least 15 gloves)
- 1 piece of cloth
- A pair of thick safety gloves
- 1 wooden box with no covering on the top (approximately 500 mm in length x 500 mm wide)
Conducting the experiment
As you proceed with your experiment, these are a few things that you need to remember:- Carefully and accurately record your observations in your project journal
- All measurements should be precise and carefully recorded, making the necessary adjustments for systematic errors.
- All data should be meticulously recorded in a table/chart.
Common Mistakes in Applying the Scientific Method
As you proceed with your experiment, these are a few things that you need to remember:- Failing to conduct the experiment
Can you believe that the most common mistake scientists make is accepting a hypothesis without doing any experiments to test it? Sometimes we might think that a hypothesis makes sense without needing to test it, but it's important to always do experiments to make sure our ideas are correct. Isn't that crazy? - Ignoring relevant data
Another mistake scientists sometimes make is ignoring or dismissing data that doesn't support the hypothesis. As a scientist, it's important to be open to the possibility that the hypothesis could be right or wrong. Sometimes a scientist might really believe that the hypothesis is true (or false), or they might feel pressure to get a certain result. In these cases, they might try to find something wrong with data that doesn't support their expectations, but not carefully check data that does support their expectations. It's important for a responsible scientist to look at all the data objectively and honestly, without bias.