Hypothesis | Hypothesis | Statistical Hypothesis Testing
01/10/2001 · Our present day over-reliance upon statistical hypothesis ..
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For market research, SEM provides an opportunity to hypothesise models of market behaviour, and to test these models statistically. In this paper, examples and case studies will be presented which show, in part, that conclusions drawn from what are now fairly standard applications of techniques such as Exploratory Factor Analysis and regression (eg as used in many customer satisfaction approaches) may be unsustainable in terms of their statistical integrity.
Many have documented the difficulty of using the current paradigm of Randomized Controlled Trials (RCTs) to test and validate the effectiveness of alternative medical systems such as Ayurveda. This paper critiques the applicability of RCTs for all clinical knowledge-seeking endeavors, of which Ayurveda research is a part. This is done by examining statistical hypothesis testing, the underlying foundation of RCTs, from a practical and philosophical perspective. In the philosophical critique, the two main worldviews of probability are that of the Bayesian and the frequentist. The frequentist worldview is a special case of the Bayesian worldview requiring the unrealistic assumptions of knowing nothing about the universe and believing that all observations are unrelated to each other. Many have claimed that the first belief is necessary for science, and this claim is debunked by comparing variations in learning with different prior beliefs. Moving beyond the Bayesian and frequentist worldviews, the notion of hypothesis testing itself is challenged on the grounds that a hypothesis is an unclear distinction, and assigning a probability on an unclear distinction is an exercise that does not lead to clarity of action. This critique is of the theory itself and not any particular application of statistical hypothesis testing. A decision-making frame is proposed as a way of both addressing this critique and transcending ideological debates on probability. An example of a Bayesian decision-making approach is shown as an alternative to statistical hypothesis testing, utilizing data from a past clinical trial that studied the effect of Aspirin on heart attacks in a sample population of doctors. As a big reason for the prevalence of RCTs in academia is legislation requiring it, the ethics of legislating the use of statistical methods for clinical research is also examined.
What is hypothesis in research? - Quora
Randomized controlled trials (RCTs) have long been the dominant method of clinical scientific inquiries. With the emergent interest to mine the wisdom of Ayurveda in a modern scientific context, research scholars have started designing RCTs to validate Ayurvedic knowledge and bring it to the mainstream. While the intent of bridging the gap between Ayurveda and modern medicine is laudable, the means of investigation merit more scrutiny, in the light of six decades of severe criticism that has been brought to bear upon the statistics that support RCTs. This scrutiny is particularly important as thought leaders, while making a justifiable call to use Ayurvedic epistemology as the basis for Ayurveda research,[–] have so far operated on the assumption that clinical research using statistical hypothesis testing has some value in its own context.
Critics of hypothesis-testing procedures have observed that a population mean is rarely exactly equal to the value in the null hypothesis and hence, by obtaining a large enough sample, virtually any null hypothesis can be rejected. Thus, it is important to distinguish between statistical significance and practical significance.
21/04/2017 · What is hypothesis in research
The object of this paper is to give Ayurveda's clinical researchers some pause by challenging the holy cow of statistical hypothesis testing from practical and philosophical perspectives. We will examine two major worldviews of probability – the Bayesian and the frequentist – and present a simple model to show how learning differs given a change in our prior beliefs. We have presented a new perspective in clinical research – that of making decisions, by borrowing distinctions from the field of decision analysis (DA), a philosophy of decision making that helps us get to clarity of action. From this perspective, it will be shown that the distinction of a “hypothesis” is unclear, and hence, placing a probability on such a distinction is devoid of meaning as it is not actionable, regardless of whether one wants to be a frequentist or a Bayesian.
First, the language of statistics is routinely confusing and misleads researchers. For instance, both “significance” and “confidence” do not mean what they normally do in English. Statistical significance has no meaning beyond a probability statement that the chance of seeing results like the one we are seeing is below 5% at the 95% confidence level, provided the null hypothesis holds. Not surprisingly, due to the overloading of a common English word, results that are significant get more attention in journals.
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Our decision in this last example was to reject the null hypothesis and conclude that the average wait time exceeds 10 minutes. However, our sample mean of 11 minutes wasn't too far off from 10. So what do you think of our conclusion? Yes, statistically there was a difference at the 5% level of significance, but are that "impressed" with the results? That is, do you think 11 minutes is really that much different from 10 minutes? Since we are sampling data we have to expect some error in our results therefore even if the true wait time was 10 minutes it would be extremely unlikely for our sample data to have mean of exactly 10 minutes. This is the difference between statistical significance and practical significance. The former is the result produced from the sample data while the latter is the practical application of those results.
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With the p-value between 0.01 and 0.025 making the p-value less than (lpha) of 0.05, we will reject the null hypothesis. We have statistical evidence at the 5% level of significance to conclude that The average emergency wait time at the hospital is more than 10 minutes.
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By implication, probabilities do not exist in the clairvoyant's world; only facts do. This implies that we cannot have distinctions with a notion of probability built into them. Cohen and Ioannidis violate this principle in their critiques, by trying to determine the chance that a hypothesis is true, when the distinction “hypothesis A is true” does not pass the clarity test, and a probability on such an unclear distinction is also unclear. The intent behind the clarity test is to distinguish between the map and the territory, for the map is not the territory. The clairvoyant can only answer questions on the territory, not on the map.
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With a test statistic of - 1.3 and p-value between 0.1 to 0.2, we fail to reject the null hypothesis at a 1% level of significance since the p-value would exceed our significance level. We conclude that there is not enough statistical evidence that indicates that the mean length of lumber differs from 8.5 feet.
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