Please Help Answer Questions!

Question

1) When you are reading the results of scientific experiments, what are some features that you would expect to see in the design of a high-quality experiment?

2) How does improving measurement accuracy help provide more valid scientific results? How does improving measurement precision help provide more valid scientific results?

3) Gina has measured that the average voltage across a chemical battery she built is 2.21 volts. If the actual value is 2.04 volts, what is Gina’s percent error?

Answer 1

Hello Ria;

To answer your second question:

Improving a measurement's accuracy means that the measured result will be closer to the what the actual value is. You can check a measurement's accuracy by measuring a carefully controlled standard and see if your measurement is close to the actual value of the standard. Thus, improving the accuracy of a measurement increases the certainty that value you get is close to the true value, even if it is unknown, making these measurements more valid than a less accurate measurement.

Improving a measurement's precision means that repeated measurements are close in value to each other. You can check a measurement's precision by repeating the measurement multiple times and assessing how close together the measurements are with a metric such as the standard deviation of all measurements. Improving the precision of a measurement means that your measurement is highly repeatable, so that if others wanted to replicate the same measurement, they would likely get similar results, increasing the scientific validity of the result.

Answer 2

Hello Ria;

For your first question;

Clear Purpose: The experiment should have a clear goal or question it's trying to answer.

Controlled Variables: The scientists keep everything the same except for the one thing they're testing. This helps see if changes are really due to what they're testing.

Independent Variable: This is the thing being tested or changed by the scientists.

Dependent Variable: The result or outcome that's measured and affected by the independent variable.

Experimental Group: This is where the independent variable is changed to see its effect.

Control Group: This is similar to the experimental group but doesn't have the independent variable. It's used for comparison.

Randomization: Participants or samples are chosen randomly to make sure the results are fair and not biased.

Replication: Other scientists can do the same experiment again to check if the results are consistent.

Data Collection: Precise measurements and observations are recorded during the experiment.

Analysis: Scientists use math and stats to understand what the data is saying.

Conclusion: They sum up what they found and whether it supports their initial idea.

Peer Review: Other experts check the experiment and results to make sure it's done well and makes sense.

References: They list the sources they used for information or inspiration.