Disadvantages of Hypothesis Testing

For example, if you found that the mean height of male Biology majors was significantly larger than that of female Biology majors, you might report this result (in blue) and your statistical conclusion (shown in red) as follows:

The alternative hypothesis might, in fact, be what we believe to betrue.

Causes should not be confused with mechanisms. For example, asbestos is a cause of mesothelioma, whereas oncogene mutation is a putative mechanism. On the basis of the existing evidence, it is likely that (a) different external exposures can act at the same mechanistic stages and (b) usually there is not a fixed and necessary sequence of mechanistic steps in the development of disease. For example, carcinogenesis is interpreted as a sequence of stochastic (probabilistic) transitions, from gene mutation to cell proliferation to gene mutation again, that eventually leads to cancer. In addition, carcinogenesis is a multifactorial process—that is, different external exposures are able to affect it and none of them is necessary in a susceptible person. This model is likely to apply to several diseases in addition to cancer.

What role do human beings play in this hypothesis.

If possible, give the key result of the study in the title, as seen in the first example.

Two groups are defined at the start of the study: an exposed group and an unexposed group. Problems of diagnostic bias will arise if the search for cases differs between these two groups. For example, consider a cohort of people exposed to an accidental release of dioxin in a given industry. For the highly exposed group, an active follow-up system is set up with medical examinations and biological monitoring at regular intervals, whereas the rest of the working population receives only routine care. It is highly likely that more disease will be identified in the group under close surveillance, which would lead to a potential over-estimation of risk.

27/09/1997 · Commentaries on Significance Testing

The article on statistics by Biggeri and Braga, as well as the title of this chapter, indicate that statistical methods cannot be separated from epidemiological research. This is because: (a) a sound understanding of statistics may provide valuable insights into the proper design of an investigation and (b) statistics and epidemiology share a common heritage, and the entire quantitative basis of epidemiology is grounded in the notion of probability (Clayton 1992; Clayton and Hills 1993). In many of the articles that follow, empirical evidence and proof of hypothesized causal relationships are evaluated using probabilistic arguments and appropriate study designs. For example, emphasis is placed on estimating the risk measure of interest, like rates or relative risks, and on the construction of confidence intervals around these estimates instead of the execution of statistical tests of probability (Poole 1987; Gardner and Altman 1989; Greenland 1990). A brief introduction to statistical reasoning using the binomial distribution is provided. Statistics should be a companion to scientific reasoning. But it is worthless in the absence of properly designed and conducted research. Statisticians and epidemiologists are aware that the choice of methods determines what and the extent to which we make observations. The thoughtful choice of design options is therefore of fundamental importance in order to ensure valid observations.

Null hypothesis significance testing- Principles

The text of the Results section should be crafted to follow this sequence and highlight the evidence needed to answer the questions/hypotheses you investigated.

What are the advantages and disadvantages of both …

As you'll see in the descriptions of particular statistical tests, sometimes it is important to decide which is the independent and which is the dependent variable; it will determine whether you should analyze your data with a or , for example. Other times you don't need to decide whether a variable is independent or dependent. For example, if you measure the nitrogen content of soil and the density of dandelion plants, you might think that nitrogen content is an independent variable and dandelion density is a dependent variable; you'd be thinking that nitrogen content might affect where dandelion plants live. But maybe dandelions use a lot of nitrogen from the soil, so it's dandelion density that should be the independent variable. Or maybe some third variable that you didn't measure, such as moisture content, affects both nitrogen content and dandelion density. For your initial experiment, which you would analyze using , you wouldn't need to classify nitrogen content or dandelion density as independent or dependent. If you found an association between the two variables, you would probably want to follow up with experiments in which you manipulated nitrogen content (making it an independent variable) and observed dandelion density (making it a dependent variable), and other experiments in which you manipulated dandelion density (making it an independent variable) and observed the change in nitrogen content (making it the dependent variable).