Define auxiliary hypothesis by Michelle Piazza - issuu

A fairly standard reply to this line of argument is to suggest thatwhat Laudan and Leplin really show is that the notion of empiricalequivalence must be applied to larger collections of beliefs thanthose traditionally identified as scientific theories—at leastlarge enough to encompass the auxiliary assumptions needed to deriveempirical predictions from them. At the extreme, perhaps this meansthat the notion of empirical equivalents (or at least timelessempirical equivalents) cannot be applied to anything less than“systems of the world” (i.e. total Quinean webs ofbelief), but even that is not fatal: what the champion of contrastiveunderdetermination asserts is that there are empirically equivalentsystems of the world that incorporate different theories ofthe nature of light, or spacetime, or whatever. On the other hand, itmight seem that quick examples like van Fraassen’s variants ofNewtonian cosmology do not serve to make this thesis asplausible as the more limited claim of empirical equivalence forindividual theories. It seems equally natural, however, to respond toLaudan and Leplin simply by conceding the variability in empiricalequivalence but insisting that this is not enough to undermine theproblem. Empirical equivalents create a serious obstacle to belief ina theory so long as there is some empirical equivalent tothat theory at any given time, but it need not be the same one at eachtime. On this line of thinking, cases like van Fraassen’sNewtonian example illustrate how easy it is for theories to admit ofempirical equivalents at any given time, and thus constitute a reasonfor thinking that there probably are or will be empirical equivalentsto any given theory at any particular time we consider it, assuringthat whenever the question of belief in a given theory arises, thechallenge posed to it by constrastive underdetermination arises aswell.

Auxiliary hypothesis example - Leipzig Reisemobilhafen

A hypothesis is an explanation for auxiliary hypothesis example a set of observations

What Is An Auxiliary Hypothesis

"One of the reasons for this state of affairs is the fact that the Efficient Markets Hypothesis, by itself, is not a well-defined and empirically refutable hypothesis. To make it operational, one must specify additional structure, e.g., investor’ preferences, information structure, business conditions, etc. But then a test of the Efficient Markets Hypothesis becomes a test of several auxiliary hypotheses as well, and a rejection of such a joint hypothesis tells us little about which aspect of the joint hypothesis is inconsistent with the data. Are stock prices too volatile because markets are inefficient, or is it due to risk aversion, or dividend smoothing? All three inferences are consistent with the data. Moreover, new statistical tests designed to distinguish among them will no doubt require auxiliary hypotheses of their own which, in turn, may be questioned."
Lo and MacKinlay (1999), pages 6-7

Efficient Markets Hypothesis: Joint Hypothesis

The alternative hypothesis is what we are attempting to demonstrate in an indirect way by the use of our hypothesis test. If the null hypothesis is rejected, then we accept the alternative hypothesis. If the null hypothesis is not rejected, then we do not accept the alternative hypothesis. 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 Fahrenheit.”

List of Reference(s) AUXILIARIES IN PERSIAN LANGUAGE AND SPLIT AUXILIARY HYPOTHESIS

The problems with p-values are not just with p-values: …

Returning to our earlier example, if the player still does not work when the batteries are replaced, this does notprove conclusively that the original batteries are not dead. This tells us that when we apply the HDmethod, we need to examine theadditional assumptions that are invoked whenderiving the predictions. If we are confident thatthe assumptions are correct, then thefalsity of the prediction would be a goodreason to reject the hypothesis. On the other hand,if the theory we are testing has been extremely successful, then we need to be extremely cautious before we reject a theory onthe basis of a single false prediction. These additional assumptions used in testing a theory are known as "auxiliary hypotheses".

Curiosity is the raw ingredient of knowledge

Duhem’s original case for holist underdetermination is, perhapsunsurprisingly, intimately bound up with his arguments forconfirmational holism: the claim that theories or hypotheses can onlybe subjected to empirical testing in groups or collections, never inisolation. The idea here is that a single scientific hypothesis doesnot by itself carry any implications about what we should expect toobserve in nature; rather, we can derive empirical consequences froman hypothesis only when it is conjoined with many other beliefs andhypotheses, including background assumptions about the world, beliefsabout how measuring instruments operate, further hypotheses about theinteractions between objects in the original hypothesis’ fieldof study and the surrounding environment, etc. For this reason, Duhemargues, when an empirical prediction turns out to be falsified, we donot know whether the fault lies with the hypothesis we originallysought to test or with one of the many other beliefs and hypothesesthat were also needed and used to generate the failed prediction:

The Hypothesis that Saves the Day

At the heart of the underdetermination of scientific theory byevidence is the simple idea that the evidence available to us at agiven time may be insufficient to determine what beliefs we shouldhold in response to it. In a textbook example, if all I know is thatyou spent $10 on apples and oranges and that apples cost $1 whileoranges cost $2, then I know that you did not buy six oranges, but Ido not know whether you bought one orange and eight apples, twooranges and six apples, and so on. A simple scientific example can befound in the rationale behind the sensible methodological adage that“correlation does not imply causation”. If watching lotsof cartoons causes children to be more violent in their playgroundbehavior, then we should (barring complications) expect to find acorrelation between levels of cartoon viewing and violent playgroundbehavior. But that is also what we would expect to find ifchildren who are prone to violence tend to enjoy and seek out cartoonsmore than other children, or if propensities to violence and increasedcartoon viewing are both caused by some third factor (like generalparental neglect or excessive consumption of Twinkies). So a highcorrelation between cartoon viewing and violent playground behavior isevidence that (by itself) simply underdetermines what weshould believe about the causal relationship between the two. But itturns out that this simple and familiar predicament only scratches thesurface of the various ways in which problems of underdeterminationcan arise in the course of scientific investigation.