can be supported or rejected by a single experiment

Collecting evidence (data). Author contributions: B.E.A. Prediction: PKD1 increases synaptic transmission. We would then expect the Response area numbers to drop. In other words, the significance level is a statistical way of demonstrating how confident you are in your conclusion. (A hypothesis is supported or rejected based on the outcome of one or more experiments.) Test: infarct size, BBB permeability, and TJP protein measurements in control and overexpression of circDGLAPA conditions. In fact, few theories fit our observations of the world perfectly. (2018). It would be worth pointing this issue out to the reader unless my logic is faulty. The figure numbers in the boxes identify the source of major data in Cen et al., 2018 that were used to test the indicated hypothesis. This is a statistical construct that is used to estimate the likelihood of reproducing a given result. For all hypothesis-testing papers, I counted the number of experimental manipulations that tested the main hypothesis, even if there were one or more subsidiary hypotheses (see example in text). Additional concerns are added by Katherine Button and colleagues (Button et al., 2013), who conclude that much experimental science, such as neuroscience, is fatally flawed because its claims are based on statistical tests that are underpowered, largely because of small experimental group sizes. Calin-Jageman and Cummings, 2019). This approach offers many direct and indirect benefits for neuroscientists thinking habits and communication practices. Moreover, we intuitively expect conclusions bolstered by several lines of evidence to be more secure than those resting on just one. Since neuroscience relies heavily on scientific hypothesis testing, I propose that it would benefit from a quantitative way of assessing hypothesis-testing projects. But a single p value cannot meaningfully represent a study involving multiple tests of a given hypothesis. Which of the following statements is true of a hypothesis? I argue that scientific hypothesis testing in general, and combining the results of several experiments in particular, may justify placing greater confidence in multiple-testing procedures than in other ways of conducting science. I emphasize that this exercise is merely intended to illustrate the combining methods; the ultimate aim is to encourage authors to explain and justify their decisions about including or excluding certain tests in their analyses. (2018) as a meta-analysis (see Borenstein et al., 2007; Cummings and Calin-Jageman, 2017) of the effect sizes, defined by Cohens d, of the same predictions. This diagram omits experimental controls tests that primarily validate techniques, include non-independent p values, or add useful but non-essential information. Task Force on Reproducibility, American Society for Cell Biology, 2014). I also argue that the issue can be dealt with by being conservative-if there is doubt about whether one test is independent of another, leave one out of the calculation. Note that this is an extremely conservative approach, as including more supportive experiments in meta-analyses typically strengthen the case. I used the p values to calculate the combined mean significance level for all of the tests according to Fishers method (see below). In this case, prompted by the Reviewers comments, I omitted control and redundant experiments that I had initially included and focused on key manipulations that were most directly aimed at testing the main hypothesis. I suggest that Fishers method, meta-analyses of effect sizes, or related procedures that concatenate results within a multitest study would be a sensible way of assessing the significance of many investigations. These large values are consistent with the results that Cen et al. Hence, there is not a single null side of a distribution. Tests (Fig. I have tried to correct the misapprehension that I inadvertently caused on the issue of PPV. Many of the concerns stem from portrayals of science like that offered by the statistician, John Ioannidis, who argues that most published research findings are false, especially in biomedical science (Ioannidis, 2005). See Page 1. I was encouraged to learn that both reviewers found merit my manuscript that might, if suitably revised, allow it to be published. I treated each prediction of the main hypothesis in Cen et al. Hence, the null hypothesis is rejected. or Bai et al., is so crucial that the main message of the papers would be destroyed if it were not replicated. Furthermore, PPV is also inversely related to the p value, ; the smaller the , the larger the PPV. The same general reasoning applies to the case in which several independent experimental predictions of a given hypothesis are tested. 38, issues 13, 2018, identified by page range (n=52). (Fig. c. Boekel et al.s study is quite small, including only five replication attempts and two are from the same group (Kanai and colleagues), which raises broad concerns about the degree to which it represents neuroscience at large, as well as issues of sample independence in this study. This is conservative, since including it would almost certainly increase the significance of the combined test. To determine the validity and importance of a multifaceted, integrated study, it is necessary to examine the study as a whole. b. However, there is currently no standard way of objectively evaluating the significance of a collection of results. It is verified by testing it. the predicted outcome always happens the hypothesis is supported the results provide useful evidence to support or reject the hypothesis no control is needed. There are a number of worrisome features of these studies: In the original study (Forstmann 1) they show a highly significant and precise (p=0.00022) correlation, r = 0.93, between the SMA measure and the Caution parameter. As the effects of individual variables are understood, scientists seek to understand more complex interactions. (2010) has been cited a respectable 328 times and Boekel et al. we proposed that N-cadherin might contribute to the cell- cell adhesion between neurons under regulation of PKD., Further, In this work, we used morphological and electrophysiological studies of cultured hippocampal neurons to demonstrate that PKD1 promotes functional synapse formation by acting upstream of N-cadherin.. WebExperiments are typically exempt when they involve rDNA that is: Not in organisms and viruses. The following reviewer(s) agreed to reveal their identity: Raymond Dingledine. Continue work to achieve a decisive result with a small, variable effect size? There are five key steps in designing an experiment: Consider your Careful experimental design is required to ensure the independence of the tests to be combined. He continues, Research is not most appropriately represented and summarized by p values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based only on p values.. Going back to the above example of mean human body temperature, the alternative hypothesis is The average adult human body temperature is not 98.6 degrees In other words, the probability, pFM, of getting their collection of p values by chance alone is<0.001, and therefore, we may be justified having confidence in the conclusion. (2) The reliability of projects whose conclusions are derived from several tests of a hypothesis cannot be meaningfully determined by checking the reliability of one test. Because I found, say, the effect sizes of drug X, were small and my ns were low, the power of my pilot tests did not permit me to reject the hypothesis that drug X was effective, with confidence. These figures include 43 statistical comparisons, many of which were controls to ensure measurement validity, or which did not critically test the hypothesis, e.g., 18 tests of spine area or miniature synaptic amplitude were supportive, but not critical. To simplify the example and avoid possibly including non-independent tests, if more than one manipulation tested a given prediction, say against a common control group, I counted only the first one. Thus, the higher levels of statistical power achievable in certain experiments will also make their predicted reliability dramatically higher than previously calculated. Estimation statistics can be accomplished with either frequentist or Bayesian methods. (Figure 3), e. Prediction: overexpression of circDGLAPA should be neuroprotective in tMCAO mice. Fishers method and similar meta-analytic devices are well-established procedures for combining the results of multiple studies of the same basic phenomenon or variable; however, what constitutes the same is not rigidly defined. 2 in Bai et al., it is difficult to know whether the whole-brain Evans Blue measurements (2A,B) and Western Blot analyses for three different proteins at three different time points (6h, 12h, and 24h) post-surgery were done on single groups of experimental animals. If an investigator wants to abandon an apparently unpromising line of investigation and also wants to avoid committing the file-drawer offense, what to do? So I assumed that the papers had a logical coherence and looked for it. (2018) paper a tangle of results (I counted 116 comparisons in the figures) and doubts that any coherent interpretation of them is to be had. The JHM IRB is authorized to review and approve For example, with four alternative hypotheses, R would be 1/4; i.e., 250 times greater than in the gene-screen case. Results that support a hypothesis can't conclusively prove that it's correct, but they do mean it's likely to be correct. Meta-analysis of the effect sizes observed in the primary tests of the main hypothesis of Cen et al. It is important that each prediction truly follow from the hypothesis being investigated and that the experimental results are genuinely independent of each other for the simple combining tests that I have discussed. the predicted relationship between the behavior measure and an MRI measurement. Although one way of assessing replicability is to ask whether or not the mean of the replicating study falls within the confidence interval of the original study (e.g. In such a case, neither theory needs to be taken as the null and the likelihood ratio can be taken as the weight of evidence favoring one or the other theory. After testing the main hypothesis, they tested related hypotheses that each had its own specific predictions. The basic argument is that, if investigators are selectively sequestering insignificant results, e.g., those with p-values > 0.05, then there will be an excess of p-values which just reach significance, between 0.04 and 0.05 because, having achieved a significant result, investigators lose motivation to try for a more significant one. The findings underscore the conclusions that (1) when evaluating the probable validity of scientific conclusions, it is necessary to take into account all of the available data that bear on the conclusion; and (2) obtaining a collection of independent experimental results that all test a given hypothesis constitutes much stronger evidence regarding the hypothesis than any single result. Every hypothesis test regardless of the population parameter involved requires the above three steps. Applying Fishers test to Cen et al.s major hypothesis (k=6; df=12), yields. responded in 2016; Cortex 74:248-252). An ancillary objective of my proposal for analysis is to encourage authors to be more straightforward in laying out the logic of their work. never appeared. Fishers method takes care of this problem, with chi2=8.9 and 40 DF the combined pFM-value is >99.9%, ie not in support of the hypothesis. They next investigate -catenin as a binding partner for N-cadherin and test the hypothesis that this binding is promoted by PKD1. Ideally, by calling attention to how scientific papers are organized, my approach will help encourage authors to be more forthright in explaining and describing their work, its purpose and logic. They authors also included many control experiments and other results which served merely to validate a particular test, and I do not include these either. 6. This is why I do not classify Cen et al.s prediction that PKD1 enhances LTP as following from their main hypothesis. I then conducted a random-effects meta-analysis on the Cohens d values with Exploratory Software for Confidence Interval (ESCI) software, which is available at https://thenewstatistics.com/itns/esci/ (Cummings and Calin-Jageman, 2017). This crucial issue often gets little attention in this context,, so Ill expand on it. Actually, whenever I talk about an hypothesis, I am really thinking simultaneously about two hypotheses. Prediction: miR-143 levels should be related to degree of stroke damage: Test: compare infarct size in tMCAO WT and miR-143 knock-down (miR-143+/-) mice. (3) PKD1 increases synapse formation. B. In the most important break-down, I initially classified 42/52 (80.8%) as hypothesis testing, on re-analysis, I classified 38/52 (73.1%) this way. It is a vicious cycle that should be broken, but in the meantime we shouldnt be surprised if individuals who see others reporting only positive results do the same. When revisions are invited, a fact-based synthesis statement explaining their decision and outlining what is needed to prepare a revision will be listed below. b) As a form of meta-science, Boekel et al.s results themselves are subject to criticism, some of which I offer below. The Method has been well-studied, is intuitively easy to grasp and, provided that the combined p-values are independent, is robust. According to a survey that I conducted (Alger, 2019; Fig. 2. support. Indeed, there are circumstances in which the outcome of a single test is intended to be decisive, for instance, in clinical trials of drugs where we need to know whether the drugs are safe and effective or not. A common obstacle to good communication is the tendency of scientific papers to omit a direct statement of the hypotheses that are being tested, which is an acute problem in papers overflowing with data and significance tests. WebA hypothesis is a proposed idea that may explain an observation or phenomena. To appreciate many of the arguments of Ioannidis, Button, and their colleagues, it is necessary to understand their concept of positive predictive value (PPV; see equation below). The solid lines connect the hypothesis and the logical predictions tested. In the last paragraph of the Discussion (p. 198) they conclude: "Overall, our study demonstrates one of the multiregulatory mechanisms of PKD1 in the late phase of neuronal development: the precise regulation of membrane N-cadherin by PKD1 is critical for synapse formation and synaptic plasticity, as shown in our working hypothesis (Fig. For all of these reasons, I do not think that the findings of Boekel et al. Although the paper reports a total of 114 p values, they do not all factor equally in the analysis.

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