Introduction to experiment design | Study design | AP Statistics | Khan Academy
Courses on Khan Academy are always 100% free. Start practicing—and saving your progress—now: https://www.khanacademy.org/math/ap-statistics/gathering-data-ap/statistics-experiments/a/principles-of-experiment-design Introduction to experiment design. Explanatory and response variables. Control and treatment groups. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/gathering-data-ap/statistics-experiments/v/introduction-to-experiment-design?utm_source=youtube&utm_medium=desc&utm_campaign=apstatistics AP Statistics on Khan Academy: Meet one of our writers for AP¨_ Statistics, Jeff. A former high school teacher for 10 years in Kalamazoo, Michigan, Jeff taught Algebra 1, Geometry, Algebra 2, Introductory Statistics, and AP¨_ Statistics. Today he's hard at work creating new exercises and articles for AP¨_ Statistics. Khan Academy is a nonprofit organization with the mission of providing a free, world-class education for anyone, anywhere. We offer quizzes, questions, instructional videos, and articles on a range of academic subjects, including math, biology, chemistry, physics, history, economics, finance, grammar, preschool learning, and more. We provide teachers with tools and data so they can help their students develop the skills, habits, and mindsets for success in school and beyond. Khan Academy has been translated into dozens of languages, and 15 million people around the globe learn on Khan Academy every month. As a 501(c)(3) nonprofit organization, we would love your help! Donate or volunteer today! Donate here: https://www.khanacademy.org/donate?utm_source=youtube&utm_medium=desc Volunteer here: https://www.khanacademy.org/contribute?utm_source=youtube&utm_medium=desc
Introduction to experiment design | Study design | AP Statistics | Khan Academy
Introduction to Experiments
In this section, the instructor introduces the concept of experiments and explains how they are conducted.
Conducting an Experiment
- High blood sugar over a three-month period results in high hemoglobin A1c levels.
- Hemoglobin A1c is used as an indicator of whether a medicine helps control blood sugar.
- The explanatory variable is whether or not the patient takes the pill, while the response variable is their A1c levels.
- The treatment group receives the medicine, while the control group receives a placebo.
Blind and Double-blind Experiments
- To avoid psychological effects, both groups receive pills that look identical.
- In a blind experiment, neither group knows which pill they received.
- In a double-blind experiment, even those administering the pills do not know which group received which pill.
- Some experiments may also be triple-blind to avoid bias during data analysis.
Measuring Results
- A1c levels are measured before and after three months to determine if there was any change due to taking the medicine.
Random Sampling and Block Design
In this section, the speaker discusses the importance of random sampling in experiments to avoid imbalances in lurking variables. They explain how to randomly assign participants into groups using a block design approach, which involves splitting participants into subgroups based on certain characteristics.
Random Sampling
- Random sampling is important to avoid an imbalance of lurking variables.
- A simple way to randomly sample is by assigning everyone a number and using a random number generator to select participants for each group.
- To avoid disproportionate representation of certain characteristics, such as gender, stratified sampling can be used.
Block Design
- Block design involves splitting participants into subgroups based on certain characteristics, such as gender.
- Participants are then randomly assigned within each subgroup to ensure balance between groups.
- Other lurking variables may also need to be considered when designing an experiment.
Interpreting Results
- The probability of getting good or bad results due purely to chance should be considered when interpreting results.
- Replication of the experiment by other researchers is important for reinforcing findings.