What is the relationship between the predicted outcome and the claim being tested in scientific experiment? An experimented outcome can be compromised in two ways. What are they and how do they differ?
The predicted outcome is a common aspect of science with a particular interest in scientific research studies. In science, questions about truth and the claims of certain phenomena are answered through empirical evidence. Nevertheless, there are instances where scientists make educated guesses on a particular test subject. A predicted outcome can be proved right based on measurable evidence in a hypothesis experiment. On the other hand, the results of the empirical test can differ from the predicted outcome making it a false claim. Usually, a predicted outcome originates from natural world observations on myriad things. The inquisitive nature of human beings leads to the creation of ideas of the way things are, resulting in hypotheses. Further observations are made through the collection of empirical data and analyzing the same in a hypothesis test. However, experimental tests are faced with myriad challenges.
One, an experiment may end up considering factors that are far-fetched, resulting into a false validation on the prediction. Overlooking certain factors could have grave consequences on the general empirical test. Therefore, measures to prevent or minimize the possibility of such an error occurring should be set in place. To control the extraneous factors, experimental conditions need to be set up. On the other hand, an experiment could overlook factors that lead to a false rejection of the prediction. Like in the first case, a scientist needs to put precautionary measures to ensure results are fair and not biased. A good experimental design should be put in place to avoid conceding the experiment.
What is the difference between an experiment group and a control group? Discuss in detail how each of them is used.
In a scientific experiment, there are two types of groups created in the procedure; the experiment group and the control group. The experiment group is the set that receives the independent variable. This means that the experiment group is specifically used to test the variable. On the other hand, the control group is the set that does not get the variable. This means no independent variable is administered to this set. However, the two sets of experiments are placed under the same conditions during the experiment. The two sets of experiments play a vital role in determining the effects of the independent variable in the test subject. The experiment group is expected to produce a predetermined change while the control group provides a base of comparison (King, Keohane, & Verba, 2010). In some cases, there is only one group in the experiment (the experimental group) thus no control group is required.
The experiment and control groups ensure that no alternate explanations in the experiment results. In this case, experimental errors and bias are limited and controlled. The control experiment is put in place to ensure the validity of experiment results. Besides, the control experiment can be used in other ways depending on the hypothesis test being carried out. It is important to have both the experiment and control group in an experiment in a situation where the experimental settings are intricate to isolate. All experiments have an experiment group, but not all experiments necessitate a control set.
What problems do conceptually vague and vague predictions pose when we are designing experiments? Explain in details.
In designing experiments, there are myriad problems that result from conceptual vague and vague predictions. Predictions of the result of a certain experiment could influence the actual outcome of the test. Conceptual vagueness could make it so hard to reject or confirm a claim. A good test is supposed to be designed in such a manner that a false confirmation or rejection of the claim is ruled out. It is difficult to predict what happens if little is understood about the test subjects. Conceptually vague notions have a significant role in designing test experiments. In most cases, when designing conceptually vague predictions, the idea is to get new and more information for further investigation.
Vague predictions affect the way the experimental design is carried out. Researchers are obligated to search and dig deep in collecting empirical data to justify, or reject the claim. Being a vague idea, there is much to be done in design and more complex methods may be suggested. Researchers are more keen and detailed in this experiment compared to the conceptually vague predictions. Experimental design forms an important aspect so as to avoid unambiguous test results. Vague prediction carries as much weight as conceptually vague ideas as they are all vague until proved otherwise after an empirical test. In as much as the results of the experiment are expected to be bias free, predictions of the trial results should be significantly observed. The design of an experiment should be done so as to achieve empirical results free from any bias.
What are single- and double- blind experiments and how do they address the problems posed by experimenter bias and experimental subject bias?
A blind experiment is a type of experiment whose information is kept from the participants until the experiment results are released. A single-blind experiment is a blind experiment where information that could skew the results is kept from the participants but known by the experimenter. In such kind of an experiment, the subjects are not aware whether they form part of the test subject. The experiment is designed in such a manner that the experimenters must possess full facts about the experiment. There are risks that accompany single-blind experiments such as experimenter bias, and expectations of the outcome. Experimenters' bias results from the interaction of the researchers with the subjects. On the other hand, the experimenters might consciously or subconsciously influence the subjects' behavior.
The double-blind experiment is a blind experiment that keeps both the experimenter and the participant in the dark as to who is the control or experimental group. These types of experiments are meant to achieve high standards about single-blind experiments. In a double experiment, neither the participants nor the experimenters know to what group they belong. Most cases, double-blind experiments are carried out in situations where the results are likely to be affected consciously, or unconsciously. In some cases, computer controlled experiments are referred to as double-blind experiment as the computer software does not have a direct bias towards either the subjects or the researchers. In the field of medicine, when researchers are testing the effectiveness of a new medicine, they ensure similarity between the placebo and actual medicine. A blind experiment, in this case, gives an honest opinion on the research.
References
Carey, S. S. (2012). A beginner's guide to scientific method (4th ed.). Boston, MA;Wadsworth: Cengage Learning.
King, G., Keohane, R. O., & Verba, S. (2010). Designing Social Inquiry: Scientific Inference in Qualitative Research. New Jersey: Princeton University Press.
Kuhn, T. S. (2012). The Structure of Scientific Revolutions: 50th Anniversary Edition. London: University of Chicago Press.
Walliman, N. (2012). Your Research Project: Designing and Planning Your Work. London: Sage.
