can be supported or rejected by a single experiment

The average number of experimental tests per hypothesis-testing paper was 7.5 {plus minus} 2.26 (n = 42, CI, 6.187,8.131) initially, and 6.08 {plus minus}2.12 (n = 38, CI, 5.406, 6.754) on re-analysis (Cohens d = 0.648). Nevertheless, by fostering thoughtful experimental design, hypothesis-based research, explicit reasoning, and reporting of experimental results, they can contribute to enhancing the reliability of neuroscience research. If a paper did not actually test predictions of a potential explanation, then I categorized it as questioning or discovery science. But, and this is absolutely crucial for the argument, strictly speaking my decision was unjustified. For convenience, I will call this parameter pFM because it is not a conventional p value. ), The Reviewer asks if all tests in Cen et al. This would provide many of the advantages of a low p value for summarizing overall strength of conclusions without the disadvantages of an extremely low p value for individual tests. In short, there is no obvious reason to think that proceeding to analyzing with a combined test would no be legitimate. I also searched predict and model (which was counted when used as a synonym for hypothesis and excluded when it referred to animal models, model systems, etc.) An indirect approach that offers investigators the ability to apply a solid, objective way of summarizing their results might be a step in right direction. The original- and re-analyses agreed reasonably well. But everyone agrees that this is nonsense; pilot studies that are hardly ever for reporting are a vital, integral part of science (Cummings and Calin-Jageman, p. 261). See Answer. (2018) did not report a pre-study power analysis, yet post hoc power (as determined by G* Power software) for the six tests discussed earlier ranged from 0.69 to 0.91 (mean=0.79), which, although much higher than the earlier estimate, is still underestimated. that lists a variety of such methods. Estimation statistics can be accomplished with either frequentist or Bayesian methods. The author declares no competing financial interests. 2. support. never appeared. The methods that the authors use, one-way ANOVA followed by a multiple-corrections comparison, should allow for data from at least one time point, say at 24h, to be used in the calculation. In 2010, the authors report replications of measurements of Supplementary Motor Area (SMA) involvement in their behavioral test, which involves a psychological construct, the LBA (Caution) parameter, which purports to account for a speed-accuracy trade off that their subjects perform when responding quickly to a perceptual task. Prediction: PKD1 increases synapse formation. I now point out that the concept of pre-study odds is not unequivocally well-defined and is therefore debatable. c.iii. (Figure 3), e. Prediction: overexpression of circDGLAPA should be neuroprotective in tMCAO mice. Meta-analytic studies can reveal the differences among studies as well as quantify their similarities. This notion is the foundation of the argument that conclusions derived from multiple experiments should be more secure those derived from one test. Nevertheless, while a debate on this point may affect the shape of the logical structure of the paper, it would not, in my opinion, support an argument that there is no such structure. No single experiment per se in either Cen et al. If the hypothesis is that GABA is the neurotransmitter at a given synapse, then we could use different groups of animals, experimental preparations, etc. And ideas that are protected from testing or are only allowed to be tested by one group with a vested Science relies on evidence - Understanding Science (Eric Rogers, 1966) A hypothesis is a conjectural statement of the relation between two or more variables. WebA well-supported theory may be accepted by scientists, even if the theory has some problems. Calculate post-hoc statistical power of Fishers Method? (3) Criticisms raised by statisticians regarding the reproducibility of neuroscience often presuppose that major scientific conclusions are based on single tests. Hypothesis testing is the process used to evaluate the strength of evidence from the sample and provides a framework for making determinations related to the population, ie, it provides a method for understanding how reliably one can extrapolate observed findings in a sample under study to the larger population from which the sample Non-quantifiable descriptive experiments contributed logically to the hypothesis test, but obviously did not count quantitatively. 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. In brief, the reproducibility rate of the RPP varied from 36 - 47%, low, but again much higher than Boekel et al. Test: Surface biotinylation assay. While ultimately the probability level for an aggregate test that a scientific community recognizes as significant will be a matter of convention, it might make sense to stipulate a relatively stringent level, say0.001 or even greater, for any parameter, e.g., pFM, that is chosen to represent a collection of tests. It follows from the statisticians assumptions about hypotheses and neuroscience practices that calculated PPVs for neuroscience research are low (Button et al., 2013). Alternatively, the calculated value of the test statistic z=1.88 lies in the rejection region, beyond the value of 1.645. 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. Investigators as Study Participants (Self-Experimentation) Provided that individual events are truly independentthe occurrence of one does not affect the occurrence of the other and the events are not correlatedthen the rule is to multiply their probabilities to get the probability of the joint, or compound, event in which all of the individual events occur together or sequentially. Nevertheless, no test is perfect. Null Hypothesis Significance Testing Categories were: Hyp-E: at least one hypothesis was fairly explicitly stated; Hyp-I: at least one hypothesis could be inferred from the logical organization of the paper and its conclusions, but was not explicitly stated; Alt-Hyp: at least one alternative hypothesis in addition to the main one was tested; # Tests: is an estimate of the number of experiments that critically tested the major (not subsidiary or other) hypothesis; Support: the tests were consistent with the main hypothesis; Reject: at least some tests explicitly falsified at least one hypothesis; Disc: a largely discovery science report, not obviously hypothesis-based; Ques: experiments attempted to answer a series of questions, not unambiguously hypothesis-based; Comp: mainly a computational modeling study, experimental data were largely material for model. In the book, I also cover critical reviews of the RPP, which identified a range of technical issues, including faithfulness of replication conditions, statistical power calculations etc., leading to the conclusion that, while reproducibility was probably lower than would be ideal, the observed rates were not so far from the expected rates as warrant declaring a crisis.. The https:// ensures that you are connecting to the Yet there is no standard objective method for quantitatively combining a group of related results. This is conservative, since including it would almost certainly increase the significance of the combined test. For example if the investigator were to select p<.8 for each 2-sided test of an hypothesis, and perform 20 such tests, the random chance that all 20 tests would be consistent with the hypothesis is 0.012. 1 answer below Questions A. However, I am not an expert in this area, and I dont see the links between the main hypothesis and the subsidiary tests as being as tight as an expert might. Meta-analysis of the effect sizes observed in the primary tests of the main hypothesis of Cen et al. 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. Instead of confronting hundreds of alternatives, in these cases, previous work has reduced the number to a few genuine hypotheses. b) As a form of meta-science, Boekel et al.s results themselves are subject to criticism, some of which I offer below. Power depends directly on effect size, which for the results reported by Cen et al. A) convert the hypothesis into a scientific law. Scientists are not always clear about the reasoning that we use to conduct, communicate, and draw conclusions from our work, and this can have adverse consequences. This could require large numbers of tests, many subjects etc., to achieve adequate power for a result of possibly dubious value, or even a situation in which the required n becomes so impractically and disappointingly large (e.g., Cummings and Calin-Jageman, 2017; p. 285) it is infeasible to proceed. In any event, the message of Boekel et al. Naturally, there may be honest disagreements in judgments about whether an experiment does test a legitimate prediction of a hypothesis, whether it actually follows necessarily and, hence, could really falsify it, etc.. The fact that many papers are beset by needless complexity is a shortcoming that I suspect both Reviewers would agree exists. Typically (27/39), the experimental tests were consistent with the overall hypothesis, while in 19 papers, at least one hypothesis was explicitly falsified or ruled out. As the Reviewer mentions there are numerous possibilities for meta-analysis. As regards more extensive replicability studies, I have extensively reviewed the Reproducibility Study: Psychology (RPP) by the Open Science Consortium, led by Nosek that attempted to reproduce 100 psychology studies (Alger, 2019, Chp. One group tests the main hypothesis and others test subsidiary hypotheses that are complementary to the main one but are not a necessary part of it. Are all tests a fair and unbiased sample of the available evidence? In a combining method, authors identify which of their experiments test the hypothesis in question and include all of those test results in their calculations. (Fig. line 129: random - an unfortunate use of the word, unless Brad actually used a randomizing technique to select the paper. 6. Appendix I The Scientific Method - Miami Applying Fishers test to Cen et al.s major hypothesis (k=6; df=12), yields. Theories are broad explanations based on many observations and The JHM IRB is authorized to review and approve For all of these reasons, I do not think that the findings of Boekel et al. Analysis of The Journal of Neuroscience Research Articles. Hypothesis Testing - Chi Squared Test First, it is critical to distinguish between, statistical and scientific hypotheses, as they are not the same thing (see Alger, 2019). 8600 Rockville Pike (5) PKD1 acts upstream of N-cadherin on synapse formation and synaptic transmission. The paper reports several distinct groups of experiments. The main hypothesis of this paper is that circular RNA DGLAPA (circDGLAPA) regulates ischemic stroke outcomes by reducing the micro RNA, miR-143, levels and preserving blood-brain barrier (BBB) integrity. Yet, deficiencies in presentation skills are correctible with training and encouragement from responsible bodies, mentors, and supervisors. Obviously, tree diagrams similar to those in Figs. Indeed, this approach allows combining in a rational way groups of results in a way that agrees with common usage and reasoning. Acknowledgements: I thank Asaf Keller for his comments on a draft of this manuscript. Hypothesis Testing Yet there is no standard, objective process for taking multiple outcomes into account when evaluating such studies. Test: BBB permeability in WT and miR-143+/- tMCAO models. 2 and 3 could be constructed for Bai et al. PPV is defined as the post-study probability that [the experimental result] is true (Button et al., 2013). My initial comments have all been addressed. 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? In fact, the overarching conclusion of each hypothesis-based investigation was supported by approximately seven experiments (6.91.57, mean SD, n=39) that tested multiple predictions of the central hypothesis. It appears that an extreme report all data standard could mean that we cant do a study without being committed to turning it into a full investigation. Fishers combined test is used in meta-analyses of multiple replications of the same experiment across a variety of conditions or laboratories but has not, to my knowledge, been used to evaluate a collection of tests of a single scientific hypothesis. 2. Not only is the work intrinsically complicated, but papers are often not written to maximize clarity. (3) PKD1 increases synapse formation. This revision includes a fuller description of my arguments, a worked example, as well as additional discussion that the original MS lacked. WebA Hypothesis Test evaluates two mutually exclusive statements, the null hypothesis and the alternative hypothesis, about a population to determine which statement is best (2018). As a library, NLM provides access to scientific literature. Which of the following statements couldnot be supported or rejected by a scientific experiment? In a well-designed experiment ___. So I assumed that the papers had a logical coherence and looked for it. The main hypothesis predicts that PKD1 associates directly with N-cadherin, and that PKD1 and N-cadherin jointly affect synaptic development in a variety of structural and physiological ways. Top panel shows individual effect sizes (corrected, dunbiased) for the tendency of small samples to overestimate true effect sizes (see Cummings and Calin-Jageman, 2017; pp 176177), Ns and degrees of freedom (df) of samples compared, together with confidence intervals (CIs) of effect sizes and relative weights (generated by ESCI and based mainly on sample size) that were assigned to each sample. It's go on to test two related mechanistic hypotheses about how miR-143 damages the BBB. 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. Despite its complexity however, I believe there is a relatively straightforward logical structure to the paper. In contrast with the type of experiment, both Reviewers of my MS found that a randomly selected JNS paper reported a highly complex set of experiments that supported its conclusion. To investigate this concern, I propose to (1) ask whether neuroscience typically does base major conclusions on single tests; (2) discuss the advantages of testing multiple predictions to evaluate a hypothesis; and (3) review ways in which multiple outcomes can be combined to assess the overall strength of a project that tests multiple predictions of one hypothesis. (Fig. claim. If the literature is skewed, the primary responsibility lies with these groups. Received 2019 Sep 4; Revised 2020 Jun 5; Accepted 2020 Jun 9. (4) PKD1 increases synaptic transmission. The large diamond at the very bottom is centered (vertical peak of diamond) at the mean effect size, while horizontal diamond peaks indicate CI for the mean. I find this sort of dis-organization in a paper deplorable and suspect the Reviewers would agree. When the authors assumed that a phenomenon had a specific explanation, then conducted experimental tests of logical predictions of that explanation, and drew a final conclusion related to the likely validity of the original explanation, I counted it as implicitly based on a hypothesis even if the words hypothesis, prediction, etc. The difficulties a reader might have in deciding which experiments are truly independent remain largely unaddressed under the current system. This diagram omits experimental controls tests that primarily validate techniques, include non-independent p values, or add useful but non-essential information. Methods for combining test results are not a panacea and, in particular, will not curb malpractice or cheating. 2.7 Drawing Conclusions and Reporting the Results How did I code the papers and was my system reliable? WebA unifying position of critics is that statistics should not lead to an accept-reject conclusion or decision, but to an estimated value with an interval estimate; this data-analysis philosophy is broadly referred to as estimation statistics. In the specific context of Cen et al. Thus, the authors go on to identify specific sites on N-cad that PKD1 binds and phorphorylates and to test the hypothesis that these sites are critical for the actions of PKD1 on N-cad. 9.2A, p. 221), the majority of scientists (70% of 444 responses) have had {less than or equal to} 1 hour of formal instruction in the scientific method and scientific thinking. I did a census of the p-values (n = 114) in Cen et al.s figures and found there were 52 at {less than or equal to} 0.001, 20 between 0.01, 18 at 0.05 and 24 non-significant at 0.05. Collecting evidence (data). Then, a critical experiment can be devised to differentiate between them. As a step in this direction, I use two very different procedures, Fishers method of combining results and meta-analysis of effect sizes (Cummings and Calin-Jageman, 2017) measured by Cohens d, which have not, as far as I know, been applied to the problem of combining outcomes in the way that we need. and test five independent predictions: that synaptic stimulation will evoke an IPSP; chemically distinct pharmacological agents will mimic and block the IPSP; immunostaining for the GABA-synthetic enzyme will be found in the pre-synaptic nerve terminal; the IPSP will not occur in a GABA receptor knock-out animal, etc. The Method has been well-studied, is intuitively easy to grasp and, provided that the combined p-values are independent, is robust. I determined effect sizes with an on-line effect-size calculator (https://www.socscistatistics.com/effectsize/default3.aspx). 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). The Scientific Method These authors suggest that a steep decrease in p value would reduce the number of false positives that can contribute to irreproducible results obtained with more relaxed significance levels. The information provided by combining tests would distinguish results expected to be more robust from those likely to be less robust. Prediction: miR-143 levels should be elevated in ALS and stroke models. WebDrawing Conclusions. Naive joint probability considerations, Fishers method, or a meta-analysis of effect sizes, all argue strongly that the aggregate probability of obtaining a given group of p values will be much smaller than any one p value. A tentative 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.. To respond to this concern, I have analyzed Bai et al., as I did Cen et al.. although I do not include this analysis in the MS. As the Reviewer infers, there is more than one hypothesis in the paper; a central one as well as ancillary hypotheses that are linked to it, but are not predicted by the main one. Placing the onus of discerning the underlying logic that shapes a project exclusively on readers is unreasonable. Solved A single experiment or observation can - Chegg The solid lines connect the hypothesis and the logical predictions tested. We would then expect the Response area numbers to drop. The main point that Boekel et al. WebThe results of a test may either support or contradictopposea hypothesis. Hypothesis Trouble: What to do when a science project fails When more than one experimental group was compared with the same standard control, I took only the first measurement to avoid possible complications of non-independent p values. Or consider that a standard random-effects meta-analysis is specifically designed to include different populations of individuals and different experimental conditions. Test: Over-expression (OE) of hPKD1 increases spine density (p<0.001); dominant negative (DN)-hPKD1 decreases spine density (p<0.001). Select one: A. Rather they reflect the factors-deficient training, imitation, improper rewards-that Ive alluded to, rather than absence of structure. was heavily dependent on the outcome of a single test; e.g. These are the kinds of data that reviewers would probably reject as meaningless, and herein lies the conundrum. Recently, a large group of eminent statisticians (Benjamin et al., 2018) has recommended that science redefine its (i.e., significance level), to p<0.005 from p<0.05. Which of the following statements is true of a hypothesis? Cohens d is a normalized, dimensionless measure of the mean difference between control and experimental values. The Reviewer alludes several times to the file-drawer problem. As noted earlier, some science criticism rests on the concept that major scientific conclusions rest on the outcome of a single p valued test. line 184-85 If the tests were done at p<.05. I get the logic of this, with the analogy of the odds of flipping 5 heads in a row for example, but it doesnt seem entirely right. 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. Answer: True. WebThe general idea of hypothesis testing involves: Making an initial assumption. The Reviewer asks about the coding that I used to classify the papers as hypothesis-testing or not. a. Boekel et al., tried to replicate five cognitive neuroscience studies, comprising 17 sub-experiments, which were mainly correlational comparisons of behavioral phenomena, e.g. Too small effect sizes and-or low power would be inconsistent with the pattern of highly significant results and might suggest questionable research practices. To see if the effect sizes and post-hoc power calculations (a priori values were not given) in Cen et al.s paper were consistent with their p-values, I measured means and standard errors roughly from the computer displays of their figures. Reviewer 1 calls the Cen et al. That usage would be analogous to taking $.50$ as your alpha. Mixing them together will not help us answer either one. To illustrate use of the statistical combining methods, I analyzed the paper by Cen et al. Cortex, 93:229-233), also from Forstmanns laboratory, published a lengthy Corrigendum in which they corrected a mistake in the post-processing pipeline and a number of other errors in Boekel et al. In contrast, my primary intention was not to champion any particular method, but rather to put forward the concept of using combination methods to summarize the different results of a multi-faceted test of a hypothesis. Upper panel also shows mean effect size for random effects model and CI for mean. The same general reasoning applies to the case in which several independent experimental predictions of a given hypothesis are tested. It is critical here to distinguish between a scientific hypothesis and a statistical hypothesis, which are very different concepts (Alger, 2019; chapter 5). In brief (details in Methods section of MS) to analyze the paper I counted as actual predictions only experiments which were capable of falsifying the hypothesis. Experts are tested by Chegg as specialists in their subject area. WebThis problem has been solved! Inclusion in an NLM database does not imply endorsement of, or agreement with, Campbell Mastering Biology: Scientific Method Flashcards Classification of research reports published in The Journal of Neuroscience, vol. As Figure 4 in the MS shows, the mean effect size (vertical peak of the diamond at the bottom) falls within the cluster of effect sizes and has a 95% confidence interval that is well above zero. I used G*power (http://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html) and found that the post-hoc power of these tests ranged from 0.69 to 0.92 (mean = 0.79). The use of combination tests that the Reviewer mentions, where the same experiment is replicated by different people using different animals, would be a conventional context for meta-analysis. B) perform additional experiments to test the hypothesis. reject If sample WebVolodymyr Zelenskyy has condemned the missile strikes on Kramatorsk which left at least four people dead. I believe these are all direct predictions of the main hypothesis; falsification of any of them would call for its rejection or revision. In fact, few theories fit our observations of the world perfectly. 2016) rather than a side. WebHypotheses are single tentative guesses, good hunches assumed for use in devising theory or planning experiments intended to be given a direct experimental test when possible. 10). The bolded quotes express, respectively, their main hypothesis, the methods they use to test it, and an overview of their conclusions about it. found r=0.03, and no p-value was reported (the Bayes factor provided some support for the null hypothesis of zero correlation). Expert Answer. I stress that Fishers method is markedly affected by any but the most minor correlations (i.e., r>0.1) among p values; notable correlations among these values will cause pFM to be much lower (i.e., more extreme) than the actual significance value (Alves and Yu, 2014; Poole et al., 2016). 5. Statistical power is essentially the ability of a test to identify a real effect when it exists. (2018). These large values are consistent with the results that Cen et al. (2010) has been cited a respectable 328 times and Boekel et al. At the core of biology and other sciences lies a problem-solving approach called the scientific method. Boekel et al.s work was done in their home country, the Netherlands, and yet three of the five studies involved measures (e.g., social media behavior) which could well show a cultural influence. 2A2,B2,D2,E2, 3B2,C2 of Cen et al., 2018). c. The Reviewer implies that the prevalence of positive results in, e.g., JNS papers, is prima facie evidence either of incredible predictive ability or bad behavior on the part of investigators.
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