Question

The _____________ is controlled by the experimenter, while the _____________ represents the information collected and statistically analyzed by the experimenter. a. dependent variable; independent variable b. independent variable; dependent variable c. placebo effect; experimenter bias d. experiment bias; placebo effect

Short answer

b. independent variable; dependent variable

Step-by-step solution

Understanding Independent Variable

An independent variable is a variable that is manipulated or controlled by an experimenter in order to investigate its potential effect on a dependent variable.

Understanding Dependent Variable

A dependent variable represents the outcome of an experiment. It's a variable that may change depending on the changes in independent variables. The changes in the dependent variable are observed and statistically analyzed by an experimenter.

Identifying Independent and Dependent Variables

According to the definitions, the variable controlled by the experimenter is the independent variable and the variable that represents the information collected and statistically analyzed by the experimenter is the dependent variable.

Key concepts

Experimental Design

Understanding the fundamental concepts of experimental design is crucial for anyone venturing into scientific research or any field that requires methodical inquiry. Experimental design refers to the framework or blueprint for conducting experiments. It involves planning ahead to ensure that the experiment is structured in a way that will yield reliable and valid results.

When talking about experimental design, it's necessary to distinguish between the independent and dependent variables. As explained in the textbook exercise, the independent variable is the one that is controlled or manipulated by the experimenter. For example, if a scientist is testing the effect of fertilizer on plant growth, the type of fertilizer used is an independent variable.

On the other hand, the dependent variable is what the experimenter observes to measure the effect of changes in the independent variable. In the same experiment on plant growth, the dependent variable could be the height of the plants. Proper experimental design also includes having a clear hypothesis, controlling variables that could affect the dependent variable, and having a sufficient sample size to reduce the chance of error.

Statistical Analysis

Statistical analysis is the cornerstone that supports the interpretation of data in experimental design. It involves applying mathematical principles and techniques to summarize, describe, and evaluate data. Once an experiment is conducted, the data collected from the dependent variables are subjected to statistical analysis to determine if there is a significant effect or correlation associated with changes in the independent variable.

Statistics help us to make inferences about our data, giving us the tools to quantify variability, assess the possibility of chance, and make predictions. For instance, in an experiment to determine whether a new teaching method is effective, statistical tests could be used to compare exam scores of students who experienced the new method against those who did not. The outcome of such analyses often leads to conclusions about the experimental hypothesis, such as confirming or rejecting it based on the evidence provided by the data.

Control Variables

A pivotal aspect of ensuring the validity of an experiment is the proper handling of control variables. These are the factors in an experimental design that are kept constant to prevent them from influencing the outcome of the experiment. Control variables are different from the independent variable, which is manipulated, and the dependent variable, which is measured.

For example, if scientists are studying the impact of light duration on plant growth (where light duration is the independent variable and plant growth is the dependent variable), they need to keep other factors like temperature, soil quality, and water intake constant. These constants are the control variables. By controlling these variables, researchers can more confidently attribute changes in the dependent variable to the manipulations of the independent variable, rather than to some other outside influence. Failure to control these variables can lead to erroneous conclusions and is often cited in improvement advice for experimental designs.