Diagram showing the relationship between Experimental Units (Subjects), Treatments, and Outcome or Response.

Illustrates the flow from experimental units to treatments and then to outcomes or responses.

Experimental Units, Treatments, Outcomes

Scientists conduct a study to determine which of three types of seed results in the largest wheat yield.

Steps in the Seed Experiment

1. Prepare three identically sized plots of land with similar soil types. 2. Plant each type of seed on a different plot, choosing the plots at random. 3. Water and fertilize the plots in the same way. 4. Harvest the wheat and measure the amount grown on each plot. 5. Compare the yields to determine the best seed type.

Diagram showing Experimental Units, Treatments, and Outcome

Visual representation of the experiment setup, including plots of land, types of seed, and measurement of growth.

Example of a Randomized Experiment

To assess the effectiveness of a new method for teaching arithmetic to elementary school children, a simple random sample of 30 first graders were taught with the new method, and another simple random sample of 30 first graders were taught with the currently used method. At the end of eight weeks, the children were given a test to assess their knowledge.

Purpose of Random Assignment in Experiments

The treatments in this experiment are the two methods of teaching. This is a randomized experiment because children were assigned to the treatment groups randomly.

An observational study is one in which the assignment to treatment groups is not made by the investigator.

Example of Observational Study

A study is performed to determine how smoking affects people’s health. A group of smokers and a group of nonsmokers are observed for several years. Scientists observe differences in health outcomes between the groups of smokers and nonsmokers.

Key Feature of Observational Study

The assignment of treatments (smoking or nonsmoking) is not made by the investigators.

Importance of randomization in experiments

Randomization helps ensure that treatment groups do not differ in any important way except for the treatments they receive. It allows for small differences among groups to be attributed to chance, and large differences to be attributed to the treatments.

Large differences in outcomes among treatment groups in a randomized experiment

Large differences in outcomes suggest that the differences are due to the treatments.

Example of a randomized experiment involving a drug called Raltegravir to reduce HIV virus levels.

This study involved dividing patients into two groups: one received Raltegravir, the other a placebo.

Results of the Raltegravir study

62% of the Raltegravir group had reduced virus levels, compared to 35% in the placebo group.

Conclusion of the Raltegravir study

The randomized experiment suggests the difference in virus reduction was due to Raltegravir.

Double-Blind Experiments

An experiment is double-blind if neither the investigators nor the subjects know who has been assigned to which treatment.

Importance of Double-Blind Experiments

When investigators or subjects know which treatment is being given, they may report results differently. Therefore, experiments should be double-blinded whenever possible.

Randomized Block Experiments

A method to restrict randomization by grouping subjects into blocks and randomly assigning treatments within each block.

Completely Randomized Experiment

An experiment with no restrictions on which subjects may be assigned which treatment.

Example of Randomized Block Experiment

Testing two reading programs in a school with grades 1-4, where each grade is a block, and students are randomized within each grade.

Observational studies may show a higher percentage of heart attacks in smokers.

This could be due to differences in the groups, such as more men in the smoking group, who are at higher risk of heart attacks.

One issue with observational studies comparing smokers and nonsmokers.

The smoking group may differ in other ways, like having more men, affecting the results.

Definition of a confounder

A confounder is a variable that is related to both the treatment and the outcome, making it difficult to determine the cause of differences in the outcome.

Example of a confounder in smoking

In the smoking example, gender was a confounder. Gender is related to smoking and heart attacks, making it hard to determine if differences in heart attack rates were due to smoking or gender.

Diagram showing the relationship between men, smoking, and heart attacks

The diagram illustrates how men are related to both smoking and heart attacks, highlighting the confounding effect.

Example of confounding in a study

In an observational study on blood pressure, people with high blood pressure were more likely to develop cancer. Age is a likely confounder, as older people tend to have higher blood pressure and are more likely to get cancer.

Role of age in confounding

Age is a likely confounder because older people tend to have higher blood pressure and are more likely to get cancer than younger people.

High blood pressure and cancer rates

People with high blood pressure may have higher cancer rates than younger people, even though high blood pressure does not cause cancer.

Confounding in observational studies

In an observational study, smokers may have higher rates of liver cancer due to their likelihood of drinking alcohol, not necessarily because of smoking itself.

Cohort studies can be divided into prospective, cross-sectional, and retrospective studies.

A type of cohort study that follows participants over time to observe future outcomes.

Retrospective Studies

A type of cohort study that looks back at events that have already occurred.

A type of observational study that compares individuals with a condition to those without.

Diagram of Observational Studies

Diagram illustrating the classification of observational studies.

Understanding Randomized Experiments and Observational Studies Flashcards

Student preview

46 questions

Show all answers

1.

FLASHCARD QUESTION

Front

Distinguish between a randomized experiment and an observational study

Back

A randomized experiment involves randomly assigning subjects to different groups to test a specific variable, while an observational study involves observing subjects without manipulation.

2.

FLASHCARD QUESTION

Front

Understand the advantages of randomized experiments

Back

Randomized experiments help eliminate bias, establish causation, and provide more reliable results by controlling for confounding variables.

3.

FLASHCARD QUESTION

Front

Understand how confounding can affect the results of an observational study

Back

Confounding occurs when an outside factor affects the results, making it difficult to determine the true relationship between variables in an observational study.

4.

FLASHCARD QUESTION

Front

Describe various types of observational studies

Back

Observational studies include cohort studies, case-control studies, and cross-sectional studies, each with different methods of data collection and analysis.

5.

FLASHCARD QUESTION

Front

Experimental units

Back

Individuals who are studied in an experiment, which can be people, animals, plants, or things. When they are people, they are sometimes called subjects.

6.

FLASHCARD QUESTION

Front

Outcome or response

Back

What is measured on each experimental unit.

7.

FLASHCARD QUESTION

Front

Treatments

Back

The procedures applied to each experimental unit.

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