Single Best Answer Questions
Vivian A. Elwell, Jonathan M. Fishman, Rajat Chowdhury in SBAs for the MRCS Part A, 2018
Concerning statistical analysis, which statement below is true?A Type I error accepts the false null hypothesis (e.g., false negative). A benefit is missed when it was there to be found.A Type II error is the incorrect rejection of a true null hypothesis (e.g., false positive). A benefit is perceived when really there is none.A Null hypothesis is a statement of no significant difference or effect.Specificity (true negative rate) measures the proportion of positives that are correctly identified as such (e.g., the percentage of people with a disease who are correctly identified as having the disease).Sensitivity (true positive rate) measures the proportion of negatives that are correctly identified as such (e.g., the percentage of healthy individuals who are correctly identified as not having the disease).
Introduction to the management station
Sukhpreet Singh Dubb in Core Surgical Training Interviews, 2020
The null hypothesis is the focus for statistical tests to disprove in a research study. The null hypothesis states that there is no difference between the two groups a researcher is investigating. If the groups were comparing rates the null hypothesis would imply that the rate of group A is equal to group B, that is 1. In a case-control study, the odds ratio in group A would equal group B, again equalling 1. This is why in a confidence interval for relative risk or odds ratio, the null hypothesis can be rejected if the interval does not include 1. For explicit variables such blood pressure and cholesterol levels, the null hypothesis would state that the value in group A is equal to the value in group B, hence A − B = 0. Therefore, the null hypothesis is rejected if the confidence interval does not include 0.
Basic Review of Parametric Statistics
Daryl S. Paulson in Applied Statistical Designs for the Researcher, 2003
Collecting valid data to provide evidence for or against the null hypothesis is crucial in statistical inference. When the evidence one collects comes from a representative sample of a larger (often much larger) group called the “population,” one can conclude that results seen in the sample-based study would hold true for the entire population. In the polio example, the researchers concluded that the Salk vaccine was effective in reducing the incidence of polio among the children who were vaccinated. Because the sample of children studied was representative of children nationwide, they were also able to conclude that, if children nationwide were given the Salk vaccine, the incidence of polio in the United States would drop significantly. And it did; because of routine vaccination, polio is now a very rare disease in industrialized countries.
Basic statistical considerations for physiology: The journal Temperature toolbox
Published in Temperature, 2019
Aaron R. Caldwell, Samuel N. Cheuvront
Currently, null hypothesis significance testing (NHST) is the predominate approach to inference in most scientific fields. In particular, environmental and occupational physiologists, whether they realize it or not, rely upon NHST which in large part is based on Jerzy Neyman and Egon Pearson’s framework for inference [10–12]. In this paradigm, the data are collected and then the scientist must decide between two competing hypotheses: the null and the alternative. In essence, we collect a sample (a group of participants) from a population (the group that the researcher is trying to study), assuming we are interested in detecting a relationship or difference of at least a certain magnitude. After the data are collected, researchers use statistical test(s) to see if the observed difference or relationship is common, assuming the null hypothesis is true. In many cases, the null hypothesis is a statement that no difference or relationship exists (i.e., nil hypothesis). However, the null hypothesis can take the form of a variety of statements. For example, a null hypothesis could be that cold-water immersion does not cool a heat-stroke patient at least 0.05 °C/min faster than ice-sheet cooling (i.e., a minimum effect hypothesis).
Effect of selective attention on auditory brainstem response
Published in Hearing, Balance and Communication, 2023
Sathish Kumar, Srikanth Nayak, Arivudai Nambi Pitchai Muthu
The data was collected from 16 subjects to test our hypothesis using three experimental conditions: active listening, passive listening with visual distracter and passive listening with the visual task. Two participants’ data were rejected in all the conditions due to the noisy EEG. We reported results using Bayesian statistics, in which the likelihoods of the null and alternative hypotheses were calculated. In our study, the null hypothesis states that there is no difference between the conditions, while the alternative hypothesis states that there is a difference. The Bayes Factor (BF) reported in the study quantifies the creditability of the hypothesis for given data. The BF10 value of more than 1 favours the alternative hypothesis, while less than 1 favours the null hypothesis. BF10 value represents the strength of evidence wherein, greater the BF10 value stronger the evidence favouring the alternative hypothesis [39].
Hypothesis-generating and confirmatory studies, Bonferroni correction, and pre-specification of trial endpoints
Published in Acta Orthopaedica, 2019
A p-value presents the outcome of a statistically tested null hypothesis. It indicates how incompatible observed data are with a statistical model defined by a null hypothesis. This hypothesis can, for example, be that 2 parameters have identical values, or that they differ by a specified amount. A low p-value shows that it is unlikely (a high p-value that it is not unlikely) that the observed data are consistent with the null hypothesis. Many null hypotheses are tested in order to generate study hypotheses for further research, others to confirm an already established study hypothesis. The difference between generating and confirming a hypothesis is crucial for the interpretation of the results. Presenting an outcome from a hypothesis-generating study as if it had been produced in a confirmatory study is misleading and represents methodological ignorance or scientific misconduct.
Related Knowledge Centers
- Bayes Factor
- Statistical Hypothesis Testing
- Statistical Inference
- Alternative Hypothesis
- Sampling
- Statistical Significance
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- Test Statistic
- Homogeneity & Heterogeneity
- Testing Hypotheses Suggested By The Data