China
Africa
Explore chapters and articles related to this topic
Basic stats
Published in O. Ajetunmobi, Making Sense of Critical Appraisal, 2021
The ‘alternative hypothesis’ is the proposed experimental hypothesis that invariably runs contrary to the null hypothesis in a study. However, when contemplating a research question, a scientist must start with the presumption that the null hypothesis actually holds true in that instance. An experiment is then devised in order to test this nonassociation prediction, that is, hypothesis testing.
Alarms and Clinical Surveillance
Published in John R. Zaleski, Clinical Surveillance, 2020
The Type I error occurs when the “evidence” leads one to reject a true null hypothesis and accept a false alternative hypothesis. An example related to alarm signal annunciations is as follows: the null hypothesis states that there is no clinically actionable event requiring intervention. But, the (false) alternative hypothesis is accepted. Hence, the alternative hypothesis is selected incorrectly: an alarm signal annunciation occurs despite the fact that there is no clinically actionable event occurring.
Knowledge Area 2: Teaching and Research
Published in Rekha Wuntakal, Ziena Abdullah, Tony Hollingworth, Get Through MRCOG Part 1, 2020
Rekha Wuntakal, Ziena Abdullah, Tony Hollingworth
With regard to hypothesis testing, which of the following is correct? Null hypothesis specifies a hypothesized real value for a parameterType I error occurs when the null hypothesis is not rejected when it is falseType II error occurs when the null hypothesis is rejected when it is trueThe power of the test is the probability of accepting the null hypothesis when it is falseAn alternative hypothesis specifies a real value for a parameter which will be considered when the null hypothesis is not rejected
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].
Progress, development, and challenges in amyotrophic lateral sclerosis clinical trials
Published in Expert Review of Neurotherapeutics, 2022
Jasmine F. Ashhurst, Sicong Tu, Hannah C. Timmins, Matthew C. Kiernan
An often overlooked aspect for improving clinical trial sensitivity is sound statistical modeling, which becomes significantly more complex than standard Fischer statistics in the context of a platform trial. A recent perspective overview outlined the potential advantages of Bayesian theorem and applied parameter estimation for the detection of statistical significance [31]. Specifically, the ability to allow for comparisons of multiple alternative hypotheses for small effects, allowing for discrimination between groups of patients or at the individual patient level. Certainly, with the increased recognition of just how much biological variability emerges as a consequence of normal aging (e.g. brain development, gut microbiome) it has become increasingly evident that accurate modeling of inter-individual differences is a critical step for advancing precision therapy [32,33].
Generalized Poisson integer-valued autoregressive processes with structural changes
Published in Journal of Applied Statistics, 2022
Chenhui Zhang, Dehui Wang, Kai Yang, Han Li, Xiaohong Wang
In this section, we will use the SCGPINAR(1) model to fit a set of crime data recorded by police car beat 12 in the file ‘PghCarBeat.csv’. The file ‘PghCarBeat.csv’ can be downloaded from the Forecasting Principles website (http://www.forecastingprinciples.com). The analyzed data set is about the monthly counts of shots in Pittsburgh. We choose the time period from January 1991 to December 2001, with a total of 132 observations. We denoted it by et al. [30], i.e. the null hypothesis and the alternative hypothesis are: 4.1, we conduct the nonlinear test under the SETGPINAR(1) model. The p-value is 0.003, which means the analyzed data set has a strong nonlinear structure. We also conduct the nonlinear test under the CP1-SCGPINAR(1) model. The resulting p-value is 0.1292. This means the analyzed data set is not suitable for the CP1-SCGPINAR(1) model. Figure 5 shows the time series and the autocorrelation function (ACF) plot of the observations. From Figure 5, we cannot see a clear trend. Therefore, we preliminarily determine that the analyzed data set is a stationary time series. To further show the series is stationary, we perform the augmented Dickey–Fuller (ADF)1 test. We obtain a p-value of 0.01. This again shows the series is stationary. Using the SMLS algorithm discussed in Section 3.3, we calculated the threshold values of 5.