Scientific Experiment: Definition & Examples

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Lesson Transcript
Instructor: Nadine James

Nadine has taught nursing for 12 years and has a PhD in Nursing research

In this lesson, you will learn about the process of a scientific experiment. First, the lesson will outline the definition and give a short history of scientific experimentation. Then the types will be described and, finally, an example of a scientific experiment will be provided.

Definition of a Scientific Experiment

You might have seen the paper-mache volcano made at home that explodes all over the kitchen. Is that an example of a scientific experiment? The answer is no, it is not. Then what is a scientific experiment? It is a test of a hypothesis. Well now, that definition seems simple doesn't it? Well, more specifically, a scientific experiment is an organized and detailed series of steps to validate or reject a hypothesis.

A hypothesis is an explanation about a phenomenon in the natural world. The scientific experiment is the third step in the scientific method. Steps in the scientific method include: an observation made, from which a hypothesis is formed; then an experiment is completed, from which there is an analysis of the experimental results - to include supporting or rejecting the hypothesis; and, in the end, it is possible that a new hypothesis is formed.

Background of Scientific Experimentation

The first to develop and use the current scientific method was an English philosopher who lived in the 17th century named Francis Bacon. He, and others after him, disagreed with the scientific method of the time known as deduction. Rather, he felt the scientific method should use induction - which is the process used today for developing a hypothesis. However, deductive methods are still used to test the hypothesis.

Inductive methods - usually referred to as inductive reasoning, are different from deductive methods. The main difference is that in inductive reasoning, the conclusions can be false even though all the premises are true. The outcome of inductive reasoning is that the conclusions are either strong or weak, not true or false. This is because there is no way to ensure that every option for the outcome has been seen. The final outcomes are described as probabilities - how probable the conclusions made are true.

Now let's move into the 20th century and the era of statisticians. Because of the statisticians, great advances in the analysis of experimental data happened. Today, we have very complex processes that analyze the data collected in scientific experiments. These analyses are performed with computer software designed specifically to analyze data. A few examples of these statistical programs are SPSS, SAS and WINKS.

Types of Scientific Experiments

There are many different ways to describe the types of scientific experiments. For the purpose of this lesson, the types of scientific experiments used in the empirical methods will be illustrated. The three main types are experimental, quasi-experimental and observational.

The highest level of scientific experiment is known as experimental, or randomized control. In this type, as the name implies, there is the greatest amount of control. There are at least two groups used in this type of scientific experiment. Each group is made up of subjects that resemble each other as close as possible, such as by age, gender, etc. Subjects can be human, animal or the environment.

As the name also implies, the subjects are randomized to a group. For the sake of clarity, the description here will use two groups, but in experiments there can be more than two groups. There are many different methods to complete randomization.

For instance, you can use a random number generator, which you can find for free on the Web. When you get your results, you can then take every other number in the column to be in the control group. The remaining go into the experimental group.

For randomized controlled experiments, everything is the same for both groups except for the independent variable. The dependent variable is the main focus of the experiment; it is what's being examined in the experiment. What's changed in the experiment is the independent variable. It's changed in the experimental group only - this is sometimes called manipulation of the independent variable. The control group does not have any changes in the independent variable.

At the end of the experiment, the scientist examines the difference between the two groups to see if there was any effect on the dependent variable. If there is a difference, it is reported as a cause-and-effect relationship. In other words, when the independent variable is manipulated, there is an effect produced. These types of experiments are very important with medications to see if medications are beneficial and not harmful. The name associated with medication experiments is Randomized Control Clinical Trial.

The second type of scientific experiment is known as quasi-experimental. It's very similar to the randomized control experiment. However, there can be a process required in the control experiment that's missing or unable to be accomplished. Sometimes, it's related to the randomization process. For example, two groups are formed but because of ethical considerations, the groups cannot be randomly assigned.

Usually, there is no manipulation of the independent variable in a quasi-experimental experiment. Instead, the main focus of the experiment is to observe how the variables respond to one another. All variables are observed and the data are collected. The main focus of this type of experiment is to identify the variables that are to remain as constant as possible, while observing the effect of variation on the other variables of interest (called explanatory variable).

At the end of this type of experiment the researchers examine the correlations between and among the variables of interest. There are many challenges to identify and evaluate all possible variables that may influence the variable of interest. Those statisticians who we talked about previously help with this process by using methods to control variables that we do not want to study. This, in turn, helps researchers examine the variable(s) of interest that may otherwise be influenced by competing variables.

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