In studies where it is especially important to avoid concluding a treatment is effective when it actually is not, the alpha may be set at a much lower value it might be set at 0.001 or even lower. For pilot studies, α is often set at 0.10 or 0.20. However, other alpha levels may also be appropriate in some circumstances.
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The most common α level chosen is 0.05, meaning the researcher is willing to take a 5% chance that a result supporting the hypothesis will be untrue in the full population. (Thus, the researcher has made an error by reporting that the experimental treatment makes a difference, when in fact, in the full population, that treatment has no effect.) In other words, alpha represents the probability of rejecting H0 when it actually is true. The α represents how much risk the researcher is willing to take that the study will conclude H1 is correct when (in the full population) it is not correct (and thus, the null hypothesis is really true). Prior to the study, in addition to stating the hypothesis, the researcher must also select the alpha (α) level at which the hypothesis will be declared “supported”. The H0 expresses the notion that there will be no effect from the experimental treatment. Essentially, the H1 is the researcher’s prediction of what will be the situation of the experimental group after the experimental treatment is applied. These are the null hypothesis (H0) and the alternative (H1) hypothesis. There are two commonly used types of hypotheses in statistics. It states, in a testable form the proposition the researcher plans to examine in a sample to be able to find out if the proposition is correct in the relevant population. Illustration of Type I and Type II errors.Ī statistical hypothesis is the researcher’s best guess as to what the result of the experiment will show.
A summary of main statistical errors frequently encountered in scientific studies is provided below ( 6- 13):
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As a result, a large number of statistical errors occur affecting the research results.Īlthough there are a variety of potential statistical errors that might occur in any kind of scientific research, it has been observed that the sources of error have changed due to the use of dedicated software that facilitates statistics in recent years. Additionally, intentionally or not, researchers tend to draw conclusions that cannot be supported by the actual study data, often due to the misuse of statistics tools ( 5). As a result, it was suggested that statistical concepts were either poorly understood or not understood at all ( 3, 4). In a study by West and Ficalora, more than two-thirds of the clinicians emphasized that “the level of biostatistics education that is provided to the medical students is not sufficient” ( 2). Although scientists have understood the importance of statistical analysis for researchers, a significant number of researchers admit that they lack adequate knowledge about statistical concepts and principles ( 1). Developed in the last 20-30 years, information technology, along with evidence-based medicine, increased the spread and applicability of statistical science. A scientific study must include statistical tools in the study, beginning from the planning stage.
Statistical analysis is a crucial part of a research.
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