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Standardizing scientific procedures becomes difficult when their subject matters are not homogeneous, and few domains outside fundamental physics are. Attempts to quantify procedures for treatment and policy decisions that we find in evidence-based practices are currently transferred to a variety of sciences such as medicine, nursing, psychology, education and social policy.
However, they often lack a certain degree of responsiveness to the peculiarities of their subjects and the local conditions to which they are applied see also section 5. Moreover, the measurement and quantification of characteristics of scientific interest is only half of the story. We also want to describe relations between the quantities and make inferences using statistical analysis.
Statistics thus helps to quantify further aspects of scientific work. We will now examine whether or not statistical analysis can proceed in a way free from personal biases and idiosyncrasies—for more detail, see the entry on philosophy of statistics. The appraisal of scientific evidence is traditionally regarded as a domain of scientific reasoning where the ideal of scientific objectivity has strong normative force, and where it is also well-entrenched in scientific practice.
Inferential statistics—the field that investigates the validity of inferences from data to theory—tries to answer this question. It is extremely influential in modern science, pervading experimental research as well as the assessment and acceptance of our most fundamental theories.
For instance, a statistical argument helped to establish the recent discovery of the Higgs Boson. We now compare the main theories of statistical evidence with respect to the objectivity of the claims they produce. They mainly differ with respect to the role of an explicitly subjective interpretation of probability.
Simultaneously held degrees of belief in different hypotheses are, however, constrained by the laws of probability. These days, the Bayesian approach is extremely influential in philosophy and rapidly gaining ground across all scientific disciplines.
For quantifying evidence for a hypothesis, Bayesian statisticians almost uniformly use the Bayes factor , that is, the ratio of prior to posterior odds in favor of a hypothesis. The Bayes factor reduces to the likelihoodist conception of evidence Royall for the case of two competing point hypotheses.
For further discussion of Bayesian measures of evidence, see Good , Sprenger and Hartmann ch. Unsurprisingly, the idea to measure scientific evidence in terms of subjective probability has met resistance. For example, the statistician Ronald A. Fisher 6—7 has argued that measuring psychological tendencies cannot be relevant for scientific inquiry and sustain claims to objectivity.
Indeed, how should scientific objectivity square with subjective degree of belief? Bayesians have responded to this challenge in various ways:. Howson and Howson and Urbach consider the objection misplaced. In the same way that deductive logic does not judge the correctness of the premises but just advises you what to infer from them, Bayesian inductive logic provides rational rules for representing uncertainty and making inductive inferences.
Choosing the premises e. Convergence or merging-of-opinion theorems guarantee that under certain circumstances, agents with very different initial attitudes who observe the same evidence will obtain similar posterior degrees of belief in the long run. However, they are asymptotic results without direct implications for inference with real-life datasets see also Earman ch. In such cases, the choice of the prior matters, and it may be beset with idiosyncratic bias and manifest social values.
Adopting a more modest stance, Sprenger accepts that Bayesian inference does not achieve the goal of objectivity in the sense of intersubjective agreement concordant objectivity , or being free of personal values, bias and subjective judgment.
However, he argues that competing schools of inference such as frequentist inference face this problem to the same degree, perhaps even worse. Moreover, some features of Bayesian inference e. According to MaxEnt, degrees of belief must be probabilistic and in sync with empirical constraints, but conditional on these constraints, they must be equivocal, that is, as middling as possible. This latter constraint amounts to maximizing the entropy of the probability distribution in question.
The MaxEnt approach eliminates various sources of subjective bias at the expense of narrowing down the range of rational degrees of belief. Thus, Bayesian inference, which analyzes statistical evidence from the vantage point of rational belief, provides only a partial answer to securing scientific objectivity from personal idiosyncrasy.
The frequentist conception of evidence is based on the idea of the statistical test of a hypothesis. Moreover, the losses associated with erroneously accepting or rejecting that hypothesis depend on the context of application which may be unbeknownst to the experimenter. Alternatively, scientists can restrict themselves to a purely evidential interpretation of hypothesis tests and leave decisions to policy-makers and regulatory agencies.
The statistician and biologist R. Fisher , proposed what later became the orthodox quantification of evidence in frequentist statistics.
The epistemological rationale is connected to the idea of severe testing Mayo : if the intervention were ineffective, we would, in all likelihood, have found data that agree better with the null hypothesis. Unlike Bayes factors, this concept of statistical evidence does not depend on personal degrees of belief.
Much valuable research is suppressed. The frequentist logic of hypothesis testing aggravates the problem because it provides a framework where all these biases can easily enter Ziliak and McCloskey ; Sprenger These radical conclusions are also confirmed by empirical findings: in many disciplines researchers fail to replicate findings by other scientific teams. See section 5. Summing up our findings, neither of the two major frameworks of statistical inference manages to eliminate all sources of personal bias and idiosyncrasy.
The Bayesian considers subjective assumptions to be an irreducible part of scientific reasoning and sees no harm in making them explicit. A defense of frequentist inference should, in our opinion, stress that the relatively rigid rules for interpreting statistical evidence facilitate communication and assessment of research results in the scientific community—something that is harder to achieve for a Bayesian.
We now turn from specific methods for stating and interpreting evidence to a radical criticism of the idea that there is a rational scientific method. In his writings of the s, Paul Feyerabend launched a profound attack on the rationality and objectivity of scientific method.
His position is exceptional in the philosophical literature since traditionally, the threat for objective and successful science is located in contextual rather than epistemic values. When the Catholic Church objected to Galilean mechanics, it had the better arguments by the standards of seventeenth-century science. With hindsight, Galilei managed to achieve groundbreaking scientific progress just because he deliberately violated rules of scientific reasoning.
Good scientific reasoning cannot be captured by rational method, as Carnap, Hempel and Popper postulated. The drawbacks of an objective, value-free and method-bound view on science and scientific method are not only epistemic. Such a view narrows down our perspective and makes us less free, open-minded, creative, and ultimately, less human in our thinking Feyerabend It is therefore neither possible nor desirable to have an objective, value-free science cf.
Feyerabend 78— As a consequence, Feyerabend sees traditional forms of inquiry about our world e. In particular, when discussing other traditions, we often project our own worldview and value judgments into them instead of making an impartial comparison 80— There is no purely rational justification for dismissing other perspectives in favor of the Western scientific worldview—the insistence on our Western approach may be as justified as insisting on absolute space and time after the Theory of Relativity.
Feyerabend argues further that scientific research is accountable to society and should be kept in check by democratic institutions, and laymen in particular. Their particular perspectives can help to determine the funding agenda and to set ethical standards for scientific inquiry, but also be useful for traditionally value-free tasks such as choosing an appropriate research method and assessing scientific evidence.
All this is not meant to say that truth loses its function as a normative concept, nor that all scientific claims are equally acceptable. Rather, Feyerabend advocates an epistemic pluralism that accepts diverse approaches to acquiring knowledge. Rather than defending a narrow and misleading ideal of objectivity, science should respect the diversity of values and traditions that drive our inquiries about the world — This would put science back into the role it had during the scientific revolution or the Enlightenment: as a liberating force that fought intellectual and political oppression by the sovereign, the nobility or the clergy.
Objections to this view are discussed at the end of section 5. This section addresses various accounts that regard scientific objectivity essentially as a function of social practices in science and the social organization of the scientific community. All these accounts reject the characterization of scientific objectivity as a function of correspondence between theories and the world, as a feature of individual reasoning practices, or as pertaining to individual studies and experiments see also Douglas Instead, they evaluate the objectivity of a collective of studies, as well as the methods and community practices that structure and guide scientific research.
More precisely, they adopt a meta-analytic perspective for assessing the reliability of scientific results section 5. The collectivist perspective is especially useful when an entire discipline enters a stage of crisis: its members become convinced that a significant proportion of findings are not trustworthy. A contemporary example of such a situation is the replication crisis , which was briefly mentioned in the previous section and concerns the reproducibility of scientific knowledge claims in a variety of different fields most prominently: psychology, biology, medicine.
Large-scale replication projects have noticed that many findings which we considered as an integral part of scientific knowledge failed to replicate in settings that were designed to mimic the original experiment as closely as possible e.
Successful attempts at replicating an experimental result have long been argued to provide evidence of freedom from particular kinds of artefacts and thus the trustworthiness of the result. Compare the entry on experiment in physics. Conversely, when observed effects can be replicated in follow-up experiments, a kind of objectivity is reached that goes beyond the ideas of freedom from personal bias, mechanical objectivity, and subject-independent measurement, discussed in section 4.
Freese and Peterson call this idea statistical objectivity. It grounds in the view that even the most scrupulous and diligent researchers cannot achieve full objectivity all by themselves. In particular, aggregating studies from different researchers may provide evidence of systematic bias and questionable research practices QRP in the published literature.
Apart from this epistemic dimension, research on statistical objectivity also has an activist dimension: methodologists urge researchers to make publicly available essential parts of their research before the data analysis starts, and to make their methods and data sources more transparent.
For example, it is conjectured that the replicability and thus objectivity of science will increase by making all data available online, by preregistering experiments, and by using the registered reports model for journal articles i. The idea is that transparency about the data set and the experimental design will make it easier to stage a replication of an experiment and to assess its methodological quality. Moreover, publicly committing to a data analysis plan beforehand will lower the rate of QRPs and of attempts to accommodate data to hypotheses rather than making proper predictions.
All in all, statistical objectivity moves the discussion of objectivity to the level of population of studies. There, it takes up and modifies several conceptions of objectivity that we have seen before: most prominently, freedom of subjective bias, which is replaced with collective bias and pernicious conventions, and the subject-independent measurement of a physical quantity, which is replaced by reproducibility of effects. Traditional notions of objectivity as faithfulness to facts or freedom of contextual values have also been challenged from a feminist perspective.
These critiques can be grouped in three major research programs: feminist epistemology, feminist standpoint theory and feminist postmodernism Crasnow The program of feminist epistemology explores the impact of sex and gender on the production of scientific knowledge. More precisely, feminist epistemology highlights the epistemic risks resulting from the systematic exclusion of women from the ranks of scientists, and the neglect of women as objects of study.
Prominent case studies are the neglect of female orgasm in biology, testing medical drugs on male participants only, focusing on male specimen when studying the social behavior of primates, and explaining human mating patterns by means of imaginary neolithic societies e. See also the entry on feminist philosophy of biology. Often but not always, feminist epistemologists go beyond pointing out what they regard as androcentric bias and reject the value-free ideal altogether—with an eye on the social and moral responsibility of scientific inquiry.
They try to show that a value-laden science can also meet important criteria for being epistemically reliable and objective e. Thus, our conception of scientific objectivity must directly engage with the social process that generates knowledge.
Longino assigns a crucial function to social systems of criticism in securing the epistemic success of science. For an epistemic community to achieve transformative criticism, there must be:. Even the most implausible beliefs might be true, and even if they are false, they might contain a grain of truth which is worth preserving or helps to better articulate true beliefs Mill [ 72]. The underlying intuition is supported by recent empirical research on the epistemic benefits of a diversity of opinions and perspectives Page By stressing the social nature of scientific knowledge, and the importance of criticism e.
Standpoint theory undertakes a more radical attack on traditional scientific objectivity. This view develops Marxist ideas to the effect that epistemic position is related to, and a product of, social position. Feminist standpoint theory builds on these ideas but focuses on gender, racial and other social relations.
But they argue more than that. Not only is perspectivality the human condition, it is also a good thing to have. This is because perspectives, especially the perspectives of underprivileged classes and groups in society, come along with epistemic benefits.
These ideas are controversial but they draw attention to the possibility that attempts to rid science of perspectives might not only be futile but also costly: they prevent scientists from having the epistemic benefits certain standpoints afford and from developing knowledge for marginalized groups in society. The perspectival stance can also explain why criteria for objectivity often vary with context: the relative importance of epistemic virtues is a matter of goals and interests—in other words, standpoint.
By endorsing a perspectival stance, feminist standpoint theory rejects classical elements of scientific objectivity such as neutrality and impartiality see section 3. This is a notable difference to feminist epistemology, which is in principle though not always in practice compatible with traditional views of objectivity.
Feminist standpoint theory is also a political project. For example, Harding , demands that scientists, their communities and their practices—in other words, the ways through which knowledge is gained—be investigated as rigorously as the object of knowledge itself.
Like Feyerabend, Harding integrates a transformation of epistemic standards in science into a broader political project of rendering science more democratic and inclusive. On the other hand, she is exposed to similar objections see also Haack Should non-scientists really have as much authority as trained scientists?
To whom does the condition of equally shared intellectual authority apply? Nor is it clear—especially in times of fake news and filter bubbles—whether it is always a good idea to subject scientific results to democratic approval. There is no guarantee arguably there are few good reasons to believe that democratized or standpoint-based science leads to more reliable theories, or better decisions for society as a whole.
So far everything we discussed was meant to apply across all or at least most of the sciences. In this section we will look at a number of specific issues that arise in the social sciences, in economics, and in evidence-based medicine. There is a long tradition in the philosophy of social science maintaining that there is a gulf in terms of both goals as well as methods between the natural and the social sciences.
See also the entries on hermeneutics and Max Weber. Understood this way, social science lacks objectivity in more than one sense. One of the more important debates concerning objectivity in the social sciences concerns the role value judgments play and, importantly, whether value-laden research entails claims about the desirability of actions. Max Weber held that the social sciences are necessarily value laden. Nevertheless, economists are adamant that economists are not in the business of telling people what they ought to value.
All knowledge of cultural reality, as may be seen, is always knowledge from particular points of view. The reason for this is twofold. First, social reality is too complex to admit of full description and explanation. So we have to select. This is because, second, in the social sciences we want to understand social phenomena in their individuality, that is, in their unique configurations that have significance for us. Values solve a selection problem. They tell us what research questions we ought to address because they inform us about the cultural importance of social phenomena:.
Only a small portion of existing concrete reality is colored by our value-conditioned interest and it alone is significant to us. It is significant because it reveals relationships which are important to use due to their connection with our values. It is important to note that Weber did not think that social and natural science were different in kind, as Dilthey and others did. Social science too examines the causes of phenomena of interest, and natural science too often seeks to explain natural phenomena in their individual constellations.
The role of causal laws is different in the two fields, however. Whereas establishing a causal law is often an end in itself in the natural sciences, in the social sciences laws play an attenuated and accompanying role as mere means to explain cultural phenomena in their uniqueness. Nevertheless, for Weber social science remains objective in at least two ways. Einstein's cosmological constant was being challenged by new evidence. Scientists are not influenced by their personal experiences, their beliefs, or the culture of which they are a part.
In , an American astronomer working at the Mt. Wilson Observatory in southern California made an important contribution to the discussion of the nature of the universe. Edwin Hubble had been at Mt. Wilson for 10 years, measuring the distances to galaxies, among other things. In the s, he was working with Milton Humason, a high school dropout and assistant at the observatory. Hubble and Humason plotted the distances they had calculated for 46 different galaxies against Slipher's recession velocity and found a linear relationship see Figure 6 Hubble, In other words, their graph showed that more distant galaxies were receding faster than closer ones, confirming the idea that the universe was indeed expanding.
This relationship, now referred to as Hubble's Law , allowed them to calculate the rate of expansion as a function of distance from the slope of the line in the graph. This rate term is now referred to as the Hubble constant. Knowing the rate at which the universe is expanding, one can calculate the age of the universe by in essence "tracing back" the most distant objects in the universe to their point of origin. Using his initial value for the expansion rate and the measured distance of the galaxies, Hubble and Humason calculated the age of the universe to be approximately 2 billion years.
Unfortunately, the calculation was inconsistent with lines of evidence from other investigations. By the time Hubble made his discovery, geologists had used radioactive dating techniques to calculate the age of Earth at about 3 billion years Rutherford, — or older than the universe itself!
Hubble had followed the process of science, so what was the problem? Even laws and constants are subject to revision in science. It soon became clear that there was a problem in the way that Hubble had calculated his constant. In the s, a German astronomer named Walter Baade took advantage of the blackouts that were ordered in response to potential attacks during World War II and used the Mt.
Wilson Observatory in Arizona to look at several objects that Hubble had interpreted as single stars. With darker surrounding skies, Baade realized that these objects were, in fact, groups of stars, and each was fainter, and thus more distant, than Hubble had calculated.
Baade doubled the distance to these objects, and in turn halved the Hubble constant and doubled the age of the universe. In , the American astronomer Allan Sandage, who had studied under Baade, looked in more detail at the brightness of stars and how that varied with distance. The new estimates developed by Baade and Sandage did not negate what Hubble had done it is still called the Hubble constant , after all , but they revised it based on new knowledge. The lasting knowledge of science is rarely the work of an individual, as building on the work of others is a critical component of the process of science.
Hubble's findings would have been limited to some interesting data on the distance to various stars had it not also built on, and incorporated, the work of Slipher. Similarly, Baade and Sandage's contribution were no less significant because they "simply" refined Hubble's earlier work. Since the s, other means of calculating the age of the universe have been developed.
For example, there are now methods for dating the age of the stars, and the oldest stars date to approximately The Wilkinson Microwave Anisotropy Probe is collecting data on cosmic microwave background radiation Figure 7. Using these data in conjunction with Einstein's theory of general relativity , scientists have calculated the age of the universe at The convergence of multiple lines of evidence on a single explanation is what creates the solid foundation of scientific knowledge.
Why should we believe what scientists say about the age of the universe? We have no written records of its creation, and no one has been able to "step outside" of the system , as astronauts did when they took pictures of Earth from space, to measure its age.
Yet the nature of the scientific process allows us to accurately state the age of the observable universe. These predictions were developed by multiple researchers and tested through multiple research methods. They have been presented to the scientific community through publications and public presentations.
And they have been confirmed and verified by many different studies. New studies, or new research methods, may be developed that might possibly cause us to refine our estimate of the age of the universe upward or downward. This is how the process of science works; it is subject to change as more information and new technologies become available. But it is not tenuous — our age estimate may be refined, but the idea of an expanding universe is unlikely to be overturned.
As evidence builds to support an idea, our confidence in that idea builds. A motto for science, a motto for life. Do an experiment, record its outcome faithfully and objectively, and make that record available for doubters. We live longer and in more comfort, and can send space probes to the edge of the solar system. Pretty darn special, huh? Some people do believe Earth is flat.
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