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Null Hypothesis Significance Testing (nhst)

Learn the basics of Null Hypothesis Significance Testing (NHST) and how it can help you identify meaningful patterns in your data.
2023-02-20

#Review of Null Hypothesis Significance Testing (NHST)

Null hypothesis significance testing (NHST) is a widely used statistical technique in biostatistics. It is a tool for assessing the probability of an observed result occurring by chance. This article will review the fundamentals of NHST, its advantages and disadvantages, and its applications in biostatistics.

##Overview of NHST

Null hypothesis significance testing (NHST) is a statistical inference technique used to determine whether a particular result is statistically significant. This technique is based on the concept of the null hypothesis, which states that there is no relationship between two variables. If the observed result is unlikely to have occurred by chance, then the null hypothesis can be rejected and an alternative (or research) hypothesis can be accepted.

A key component of NHST is the calculation of the probability of the observed result occurring by chance. This is known as the p-value, and it is calculated by using a statistical test such as a t-test or a chi-square test. If the p-value is below a certain threshold (typically 0.05), then the null hypothesis is rejected and the alternative hypothesis is accepted.

##Advantages of NHST

NHST has several advantages that make it an attractive choice for biostatisticians.

One of the main advantages of NHST is that it provides a straightforward and easy-to-understand way of assessing the probability of an observed result occurring by chance. This makes it a useful tool for making decisions about the validity of research results, as well as for interpreting the results of a study.

Another advantage of NHST is that it is relatively simple to implement. This makes it an efficient and cost-effective approach to assessing the significance of a research result.

Finally, NHST is widely used in biostatistics, which means that there is a large body of literature and resources available on the topic. This makes it easy for researchers to access information and advice on how to use NHST in their own studies.

##Disadvantages of NHST

Despite its many advantages, NHST also has some disadvantages that should be taken into consideration by biostatisticians.

One of the main drawbacks of NHST is that it relies on the assumption of the null hypothesis, which is sometimes difficult to justify. This means that the results of NHST may not be as reliable as they could be.

Another disadvantage is that NHST can be prone to errors of interpretation. This is because the p-value is only one measure of the probability of an observed result occurring by chance, and other factors such as sample size and the use of statistical tests can also influence the results.

Finally, NHST is not suitable for all types of research. For example, it is not suitable for studies with small sample sizes or studies with non-normal data.

##Applications of NHST in Biostatistics

NHST is widely used in biostatistics to assess the significance of results from clinical studies, epidemiological studies, and experiments.

In clinical studies, NHST can be used to assess the significance of differences between groups in terms of disease risk or treatment outcomes. For example, a study may compare the risk of developing a particular disease among two groups of people, or compare the efficacy of two treatments for a particular condition.

In epidemiological studies, NHST can be used to assess the significance of associations between risk factors and disease outcomes. For example, a study may investigate the association between smoking and the risk of developing lung cancer.

Finally, in experiments, NHST can be used to assess the significance of the effect of a particular factor on an outcome. For example, a study may investigate the effect of an experimental drug on the survival rate of cancer patients.

##Conclusion

In conclusion, null hypothesis significance testing (NHST) is a widely used statistical technique in biostatistics. It is a tool for assessing the probability of an observed result occurring by chance, and it has several advantages such as being straightforward and easy to implement. However, it also has some disadvantages such as relying on the assumption of the null hypothesis and being prone to errors of interpretation. NHST is widely used in biostatistics to assess the significance of results from clinical studies, epidemiological studies, and experiments.

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