Biology Skills: Reliability Part 3

This video tutorial covers the concept of reliability in scientific experiments, focusing on reducing random errors. It explains the sources of random errors, such as environmental conditions, experimental factors, and intrinsic variability. The video provides methods to improve reliability, including increasing trials, removing outliers, and calculating averages. It uses examples like Robin Hood's archery and a catalase experiment to illustrate these concepts. A sample question on fungicide effectiveness is discussed to demonstrate reliability improvement strategies.

What is the primary characteristic of a reliable measurement in an experiment?

• It is performed quickly.

• It gives consistent results each time.

• It uses advanced technology.

• It is always accurate.

What is the relationship between precision and reliability in an experiment?

• Reliability ensures precision.

• Precision decreases reliability.

• If results are precise, the method is reliable.

• Precision is unrelated to reliability.

Which of the following best describes a random error?

• An error that is always the same size.

• An error that is caused by human mistakes.

• An error that varies in size and direction unpredictably.

• An error that occurs due to faulty equipment.

Which of the following is NOT a source of random error?

• Human error

• Experimental factors

• Environmental conditions

• Intrinsic variability

What is one method to reduce the impact of random errors in experiments?

• Reducing the sample size.

• Changing the experimental method.

• Increasing the number of trials or samples.

• Using more precise equipment.

What is the minimum number of trials recommended for checking data reliability in school experiments?

• Five trials

• Three trials

• Two trials

• One trial

Why is it important to identify outliers in experimental data?

• They are necessary for calculating averages.

• They are the most accurate data points.

• They can skew the results and affect reliability.

• They always indicate a successful experiment.

In the context of experiments, what is an outlier?

• A result that is abnormally large or small

• A result that is within the expected range

• A result that confirms the hypothesis

• A result that is consistent with others

How can calculating the average of repeated measurements help in experiments?

• It eliminates all errors.

• It highlights the most accurate measurement.

• It increases the number of data points.

• It smooths out variations caused by random errors.

Why should outliers be excluded when calculating the average in experiments?

• They are always incorrect.

• They can distort the average, making it unreliable.

• They are necessary for a complete analysis.

• They do not affect the average.