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How to Develop and Write Hypotheses
Published in Lisa Chasan-Taber, Writing Grant Proposals in Epidemiology, Preventive Medicine, and Biostatistics, 2022
The terms significant and significance most commonly refer to tests of statistical significance used by most empirical studies but can also refer to clinical significance. Just as we avoid making precise statistical predictions in hypotheses (see Tip #6: Remove Any Unnecessary Words), we also avoid the use of the terms significant or significance in hypotheses. Instead, you will describe the techniques that you will use to assess statistical significance, clinical significance, as well as the role of bias and confounding in the methods section of the proposal. Statistical significance refers to the probability of observing your study's results, or results even further from the null hypothesis, if the null hypothesis was true and is typically set at p < 0.05. Statistical significance is impacted by a number of factors including the sample size of your study (i.e., the larger the sample, the more likely that the findings will be statistically significant). In addition, even if you observe statistically significant results (i.e., p < 0.05), this does not rule out that your results might be due to bias (e.g., confounding, selection bias).
Experimental Design, Evaluation Methods, Data Analysis, Publication, and Research Ethics
Published in Yuehuei H. An, Richard J. Friedman, Animal Models in Orthopaedic Research, 2020
Sample size is one parameter which helps to determine the power, effect size (or magnitude of the effect) and level of significance of a study.12 The power of a study is the likelihood of rejecting the null hypothesis. An 80% level is generally viewed as adequate. Effect size is a measure of the difference among the groups. Cohen15 defines a small effect as 0.2 of a standard deviation, a moderate effect as 0.5 of a SD, and a large effect as 0.8. It is more difficult to detect a small effect of the independent variable than it is to detect a large effect. So, if a small difference is expected between the control and treatment group, a relatively large sample size is necessary. The significance level is the probability of rejecting a true null hypothesis; it is often set at 0.05. In the book by Cohen,15 both power and sample size tables can be found. When planning a study, the researcher should determine the desired power, acceptable significance level, and expected effect size and use these three parameters to determine the necessary sample size.12
Fundamentals
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
A hypothesis is a testable statement about a relationship between independent and dependent variables. A null hypothesis denoted by H0, is a statement that can be validated or invalidated. An alternate hypothesis, denoted by H1, is another statement that holds if the null hypothesis is invalidated. An example of a null hypothesis is that a new drug developed is more effective than the existing drug. A corresponding alternate hypothesis would be “newly developed drug is not more effective than the existing drug.”
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].
Factors influencing decisions about neurogenic bladder and bowel surgeries among veterans and civilians with spinal cord injury
Published in The Journal of Spinal Cord Medicine, 2023
Denise G. Tate, Edward J. Rohn, Martin Forchheimer, Suzanne Walsh, Lisa DiPonio, Gianna M. Rodriguez, Anne P. Cameron
The narratives of the DM process for surgery following NBB dysfunction in this sample may not be representative of veterans and civilians with SCI more broadly, due to the study’s small sample size and the fact that most study participants were from one geographic area. These DM processes may be particular to the type of surgeries described here in terms of common practices at healthcare facilities, socioeconomic structures, or geography. There is also the possibility of bias in the selection of quotes or categorizations of DM enactment styles based on the narrative provided. Individual interviews can only present part of a person’s larger life narrative. Also, quantitative comparisons were based on a relatively small sample size and need to be interpreted with caution. These analyses were conducted to highlight trends in the data rather than to draw hypotheses-driven conclusions. Since these were exploratory in nature, correction for the conduct of multiple tests, was deemed unnecessary. While Type I and Type II errors may have occurred, since null hypotheses testing was not a primary study purpose, this does not affect the primary findings. Our findings are based only on participants who decided to have surgery and thus findings do not address decisions not to have surgery. Finally, the time between these surgeries and the data collection varied greatly. Those with a longer time since surgery may have had less ability to recall details, emotions, and feelings associated with DM. Longer times since surgery may have also allowed some participants to be more detached in their assessments.
Evaluation of the GeneXpert MTB/RIF assay performance in sputum samples with various characteristics from presumed pulmonary tuberculosis patients in Shiselweni region, Eswatini
Published in Infectious Diseases, 2022
Durbbin Lupiya Mulengwa, Maropeng Charles Monyama, Sogolo Lucky Lebelo
All samples tested on GeneXpert MTB/RIF assay were also processed on MGIT culture. Statistical analysis was performed to determine sensitivity and specificity as well as positive and negative predictive values on both GeneXpert MTB/RIF and MGIT culture. The sensitivity was defined as the ability of the test to correctly identify those patients (or samples) with the disease. Specificity was defined as the test’s ability to correctly identify those patients (or sputum samples without the disease. The positive predictive value was described as the probability that subjects with a positive screening test truly have the disease while the negative predictive value is the probability that subjects with a negative screening test truly don't have the disease. The likelihood ratio was defined as how much more likely was it that a patient (or sample obtained which tests positive has the disease compared with one that tests negative. To measure the effects of each characteristic on the GeneXpert MTB/RIF positive results, a univariate and multivariate analysis was performed using simple logistic regression and multiple linear regression respectively. The difference was declared as statistically significant if P-value was less than .05. P-value is the probability of obtaining results as extreme as the observed results of a statistical hypothesis assuming that the null hypothesis is correct.