- Will require 2 peer replies after you answer this below question
- Describe the difference between an independent experimental design and a paired design.
- Explain how type I and II errors influence the construction of a hypothesis.
Reply 1): Independent experiment design refers to the design whereby various participants are factored in/used in all conditions of the independent variable. What this means is that every experiment condition is inclusive of a different participant group. This ought to be accomplished by the use of random allocation which makes sure that all participants have equal chances of being assigned to individual groups (Tan et al., 2018). The independent measures comprise of the use of two separate participants groups ; one in every condition. The advantages are that there aren’t any order effects whereby participants end up behaving differently. On the other hand, paired design refers to the experimental design whereby the participants pairs are matched on the basis of key variables for instance socio economic status or age. Here, one member for every pair then ends up being placed into the control group while the other is placed in the experimental group (Tan et al., 2018) . One member of every matched pair ought to be randomly assigned to the control group while the other to the experimental group.
Type II error explains the statistical terms that’s utilized in the context of hypothesis testing and that defines the error which happens once a user accepts a null hypothesis. This type of error produces a false negative which is often regarded as error of emission. For instance, a disease test might report negative results whereas in fact, the patient is infected (Rizzolli, 2019). It’s a type II error since the conclusion of the test as being negative has been accepted despite being incorrect. On the other hand, type I error is seen to be the rejection of true null hypothesis .Type I error rejects the alternative hypothesis despite the occurrence not happening because of chance.
References
Rizzolli, M. (2019). Type-I and Type-II errors. Encyclopedia of Law and Economics, 2112-2120. https://doi.org/10.1007/978-1-4614-7753-2_66
Tan, P., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Introduction to data mining.
2) Difference Between an Independent Experimental Design and A Paired Design.
A given study may produce measurements that are paired or that are totally independent. Statistical tests should be chosen accordingly. For example, we are interested in studying the effect of medical treatment on insulin rate. Here are two possible experimental designs that would help answer this question:
– Insulin rate is measured on 30 patients before and after the medical treatment. Data are thus organized by pairs (every patient is associated with two measurements). An appropriate test to use here would be a paired two-sample t-test.
– Insulin rate is measured on 30 patients receiving placebo and 30 other patients receiving the medical treatment. In this case, all of the measurements are independent. An appropriate test to use here would be an independent two-sample t-test.
How Type I and II Errors Influence the Construction of a Hypothesis.
In statistical analysis, a type I error is the rejection of a true null hypothesis, whereas a type II error describes the error that occurs when one fails to reject a null hypothesis that is actually false. The error rejects the alternative hypothesis, even though it does not occur due to chance (González-Ortega et al., 2021).
References:
González-Ortega, J., Ríos Insua, D., Ruggeri, F., & Soyer, R. (2021). Hypothesis testing in presence of adversaries. The American Statistician, 75(1), 31-40.
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