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Critical Thinking Fallacies Flashcards | Quizlet

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Start studying Critical Thinking Fallacies. Learn vocabulary, terms, and more with flashcards, games, and other study tools.

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False dichotomy - is a type of informal fallacy in which something is falsely claimed to be an "either/or" situation, when in fact there is at least one additional option.

False compromise ?

Ambiguity is a type of uncertainty of meaning in which several interpretations are plausible. It is thus an attribute of any idea or statement whose intended meaning cannot be definitively resolved according to a rule or process with a finite number of steps.

Framing

Conformation bias

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Prejudices, purely emotional responses, and empirically implausible positions are not the only sorts of reasons that are unacceptable in moral discourse – blind appeals to authority may well go on the list as well – but they will be enough to give a sense of the constraints posed by the requirement for reason giving.

Perhaps most importantly, the role of reasons imposes a constraint of consistency. Consistency requires that if there are exactly the same reasons in support of one course of action as there are in support of another, then those actions will be equally right or equally wrong – they will be equally well supported or undercut by reasons.

If I object to racism on the grounds that ‘all people are equal’ then I must also object to those manifestations of other prejudices, such as sexism, that deny that principle: If I do not then it will seem that in one or other or both of the cases I am not really accepting ‘all people are equal’ as a reason at all. My position will not, in fact, be based on the reason or reasons I cite.

Good ethical reasoners must be able to reason logically, to avoid fallacies and inconsistencies, to clarify and analyze concepts, to construct and assess arguments and positions.

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http://philosophy.lander.edu/logic/tvs_quiz.html

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Free Evaluating Arguments Online Practice Tests

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Overview:

This course provides an introduction to exploring and understanding arguments by explaining what the parts of an argument are, and how to break arguments into their parts and create diagrams to show how those parts relate to each other.

Argument diagramming is a great visual tool for evaluating claims that people make. By the end of the course, you will be able to think critically about arguments or claims and determine whether or not they are logical. This skill can be used in a variety of situations, such as listening to the news, reading an article, or making a point in a meeting.

This is an introductory course and may be useful to a broad range of students.

Additional Course Details

Topics Covered:

Creating Argument Diagrams, Evaluating Arguments, and Argument Diagramming for Interpreting Public Arguments and Longer Texts.

Estimated Time to Complete Course:

This course provides a two-week exploration of the task of Argument Diagramming.

Additional Software or Materials Required:

You will need Flash and Java installed. These programs are free. More detailed information is provided in the course under "Test and Configure Your System."

Course Last Updated Date:

August 2011

Changes in This Update Include:

Addition of instructional material and activities on Creating Argument Diagrams, including the integration of iLogos, an argument diagramming software created by Dr. Matthew Easterday at Northwestern University, providing practice opportunities in the construction of argument diagrams.

Addition of modules about 1) evaluating arguments and 2) rhetorical features of arguments.

In-Depth Description

The current version of this course contains 3 modules:

1. Creating Argument Diagrams

2. Evaluating Arguments

3. Argument Diagramming for Interpreting Public Arguments and Longer Texts

Module 1

Introduces the concept of argument diagramming as a way to visually represent the content and structure of an argument. It focuses on basic vocabulary and methods for determining argument structure. Each section of the module has a set of exercises for which students receive hints if they are stuck, and immediate feedback on their answers. Module 1 also introduces iLogos, the built-in argument diagramming software. This software allows students to build argument diagrams easily and quickly.

Module 2

Introduces basic logical concepts such as validity and strength, and explores ways of evaluating arguments according to these logical standards. Arguments are classified as either deductive or non-deductive. In the section on deductive arguments, students are introduced to common argument forms, such as modus ponens, and common logical fallacies, such as the fallacy of affirming the consequent. In the section on non-deductive arguments, students are introduced to 4 basic argument types: statistical arguments, inductive arguments, abductive arguments, and arguments by analogy.

Module 3

Builds on the skills that are introduced in Module 1, to introduce ways to tackle longer, more complex arguments. Students are introduced to basic rhetorical vocabulary and classifications, such as genre and exigence. Students are taught, through examples and tips, how to use these classifications to build a reasonable representation of the content and structure of arguments embedded in longer texts.

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This course provides an introduction to exploring and understanding arguments by explaining what the parts of an argument are, and how to break arguments into their parts and create diagrams to show how those parts relate to each other. Argument diagramming is a great visual tool for evaluating claims that people make.

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Subjectivists claim that ethical statements report things about the utterer rather than about the world.

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Ethics then is not just a matter of taste. We can make sense of the idea of genuine moral disagreement, it seems to make perfectly good sense to try to convince people they are mistaken about ethical matters, and we change our minds about such matters in response to argument and reason.

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Thee right action is the one that generates the most utility, understood by early proponents as the greatest happiness for the greatest number of people. The theory claims that right actions have a common property – their tendency to generate utility.

An important alternative theory responds to the fact that utilitarianism makes moral rightness depend upon what in fact would make people happy. According to Immanuel Kant, the ultimate principle of morality must be capable of guiding us to the right action in any circumstances. He tells us to ask if the motive we are considering acting from could become a universal law, a law to be followed by anyone in any situation. He is concerned with consistency and rationality, not consequences.

A further alternative theory downplays rules and principles, emphasising the importance of ‘practical wisdom’ in particular cases. The right action, according to Aristotelian virtue theory, is the action that would be chosen by an agent who has practical wisdom. But how are we to identify the person of practical wisdom? We can’t do so by seeing if they choose the right action, since the right action is the action they choose! (if we can work out whether they’ve chosen the right action on some other ground – utility maximization or respect for persons, perhaps? – that would be the test for right action, not the choice of the person of practical wisdom).

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A valid deductive argument with a moral conclusion must have at least one moral statement in the premises to the argument.

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gives an account of how the world is without saying whether that's good or bad.

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expresses an evaluation, saying that something is good or bad, better or worse, relative to some standard or alternative.

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All moral statements are normative statements.

But not all normative statements are moral statements.

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historical

substantive

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1. The authority has to have sufficient expertise.

2. It has to act within its area of expertise.

3. Its claims have to be consistent with a reasonable consensus in the area.

4. It has to act sincerely and free of bias.

5. It is only OK to resort to an authority if the reasoner themselves lacks expertise.

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“The “no acid, no ulcer” consensus was treated as a default position, to be abandoned reluctantly, and only in the face of overwhelming evidence. The mistake, we suggest, was essentially getting the allocation of the burden of proof wrong, placing too heavy a burden on the bacterial theory”.

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To have the burden of proof is to be rationally required to produce evidence for your assertion claims.

Burdens of proof come in degrees.

To have a heavier burden of proof than some opponent is to be rationally required to produce more or better evidence for your assertion claims than your opponent is rationally required to produce.

The legal example: People accused of crimes are presumed innocent. The burden of proving that they are guilty rests on the prosecutor. The accused doesn’t have to prove anything. If the prosecutor doesn’t meet the burden, the presumption that the accused is innocent stands: Innocent until proven guilty.

We think unequal allocation of the burden of proof is only legitimate in truth-directed practices in the unusual situation in which such an allocation does make it easier to establish the truth (such as where one party has exclusive access to evidence (the Tax case), or the motives for enquiring in an issue are unusually divided between the parties (the buyer-beware case). We think allocating the burden of proof equally fits better with the ideas that good reasoners should always be able to provide reasons for their assertions; that argument is ‘truth-seeking’: concerned to lead us to hold true beliefs and reject false beliefs; that in truth directed inquiries you are almost never entitled to rely upon your opponent’s inability to show that their view is true.

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P is similar to Q in certain known respects.

P has some further feature– R.

Therefore, probably,

Q also has the feature R.

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Scientific theories are not ‘mere’ theories – untested, tentative, vague generalisations. They are, instead, comprehensive, supported by large bodies of converging evidence, based on repeated observations, usually integrating and generalising hypotheses and making consistently accurate predictions across a broad area of scientific inquiry

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Scientific claims are falsifiable: there are observable outcomes that would be impossible if the claim were true.

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A technical term used to describe a comprehensive account of some aspect of nature, supported by a large body of converging evidence, based on repeated observations and testing, usually integrating and generalising hypotheses, and making consistently accurate predictions across a broad area of scientific inquiry.

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Scientific thinking is a subset of logical and critical thinking

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Scientific thinking is a subset of logical and critical thinking

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Scientific practice includes particular processes and methods designed specifically to remove the influence of at least the more common and serious obstacles to good logical and critical thinking as done by scientists. 

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Scientific practice includes particular processes and methods designed specifically to remove the influence of at least the more common and serious obstacles to good logical and critical thinking as done by scientists. 

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Explanations aren't arguments.

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Inference to the best explanation (is a non-deductive argument

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Inference to the best explanation (is a non-deductive argument

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S is a state of affairs, a collection of data, facts, observations, givens.

A hypothesis, H, would, if true, explain S.

No other hypothesis can explain S as well as H does.

Therefore, probably,

H is true.

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Non-falsifiable hypotheses are not scientific.

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Non-falsifiable hypotheses are not scientific.

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When I started this blog, I wanted it to be entirely about science and the fundamental concepts of logic. As such, I decided that topics like religion and politics where off limits. However, anytime that a politician makes a statement about science, they enter my territory and I feel obliged to deal with their comments (by the same token, I only talk about religion when people use it to make claims about science). One of the most common and irritating ways that this occurs is when politicians try to compare modern science (usually climate research) to the idea that the earth is flat and/or we are the canter of the universe. This argument is used ad nauseum and I have dealt with it before. Nevertheless, it is once again featuring prominently in our political dialogue, so I feel the need to explain why it is utter nonsense yet again.

I am, of course, referring to the recent comments by Trump advisor Anthony Scaramucci on CNN. When asked about the scientific consensus on climate change, he made several hopelessly ignorant comments like, "There was an overwhelming science that the Earth was flat, and there was an overwhelming science that we were the center of the world." Statements like that are, however, absurd for numerous reasons.

First, and most obviously, they are blatantly false. Science is an extremely careful, empirical, and systematic process of making observations, forming hypotheses, and empirically testing those hypotheses by using repeatable, controlled* experiments, and that system is an extremely recent one. It has only existed in its modern form for the last few centuries. In contrast, we have known that the earth was round since the ancient Greeks! So there can't possibly have been a scientific consensus that the earth was flat because science as we know it did not even exist then! Similarly, the idea that we are the center of the universe was discredited long before modern science, and the idea was largely based on religion. Indeed, although Galileo slightly predates modern science, when he demonstrated that the earth was not the center of the universe, he did so by applying the same basic type of systematic observations and reasoning that scientists use today. Thus, although modern science never said that the earth was the center of the universe, scientific reasoning was the thing that discredited that idea.

I want to elaborate on that last point for a second, because I want to be clear that I am not suggesting that there were no scientists before the modern era. There certainly were, but science was not the formalized discipline that it is now. In other words, although there were people who were making observations, testing hypotheses, etc., there was not a standard for the type of reasoning that qualified as science, nor was there the vast body of philosophical thought about the nature of science that we have today. My point is that the "science" that said that the earth is flat and the earth is the center of the universe is not actually the same thing that we know as science today, thus the comparison is invalid (i.e., it is an equivocation fallacy because it uses the word "science" to mean two different things).

Further, so far I have only been talking about the general concept of what science is, but we also have to take into account the tools that are available to scientists. For example, the statistics and mathematical formulas that we use today have only been around for about 100 years (sometimes much less than that). Those mathematics are extremely important because they are what allow us to rigorously examine our data and assign confidence values to our results. Similarly, today we can use computers to analyze millions of data points, run massive simulations, and do analyses that were unimaginable to scientists just a few decades ago. Further, our ability to accurately make the measurements that we use in those analyses has also increased astronomically. For example, we now have satellites that can continuously collect very precise data from all of the world. Tools like that give us incredible power for understanding our planet and the universe in general, and they give modern science a major advantage over past scientific endeavors.

There is also another problem here, and it is a more fundamental one. If this argument actually worked, then you could use it any time that you wanted, rather than simply for climate change. In other words, if the fact that people used to think that the earth was flat actually gave you cate blanche to reject science, then you would never have to accept any scientific result that you didn't like. For example, I could say, "scientists say that washing your hands helps prevent the spread of disease, but they were wrong about the earth being flat, so I don't have to listen to them when they say that we should wash our hands." That is obviously absurd, but that is exactly the same thing that Scaramucci did. His argument was logically identical, which means that if either of those arguments worked, then both should work, and the fact that the hand washing example is clearly absurd means that the climate change argument must also be absurd. That's how logic works.

Nevertheless, you might be tempted to think, "but scientists aren't completely, 100% sure that we are causing climate change, so it is always possible that they are wrong." Technically, that is true, but here is the important qualifier: science never gives us 100% certainty. It is inherently a skeptical process, and it only tells us what is most likely true given the current evidence, not what is absolutely true (details here). So sure, it is true that I can't be 100% sure that we are causing climate change, but I also can't 100% sure that the earth is round. Indeed, I can't be 100% sure that you even exist, nor can I be 100% sure that Donald Trump isn't an alien squid from another universe. However, the fact that I can't be 100% sure about those things does not mean that I get to have any form of practical doubt about them. It would clearly be ridiculous for me to actually think that Trump is an alien squid, and politicians should not make laws based on that notion. Even so, the fact that we can't be 100% sure about climate change does not mean that you get to have any practical doubt about it, nor should we be basing laws on that doubt.

In technical terms, what I have been describing is known as an argument from ignorance fallacy. It can occur in many ways, but one of its forms happens when you say, "we can't be sure that X is true, therefore X is false" or "we can't be sure that X is true, therefore it is reasonable to believe that it isn't true." This is not logically valid. The fact that we can't be completely certain of a conclusion does not mean that you can assume that all of the evidence supporting that conclusion is false. This comes back to one of my favorite topics: the burden of proof. The person making a claim is always responsible for providing the evidence to support that claim. In other words, if you want to claim that we aren't causing climate change, then you must provide actual evidence that we aren't. You can't simply assert that we can't be 100% certain, the models might be wrong, etc. If you want to claim that all of the scientific evidence is wrong, then you have to provide evidence to support that claim, not speculations. Further, although it is true that science never proves anything with 100% certainty, there are some topics that are so well supported by so many lines of evidence that it is almost unthinkable that they could actually be totally wrong, and anthropogenic climate change is one of those topics (others include atomic theory, the germ theory of diseases, etc.).

This brings me to the final topic that I want to discuss here: the actual evidence for climate change. You see, so far I have been talking in pretty general terms about science, but Scaramucci's statements are particularly absurd and misguided when we focus in on climate change, because there is so much evidence supporting it. As I have explained in more detail elsewhere, we have tested the sun, volcanoes, and other natural drivers of climate change, and they can't explain the current warming by themselves ( Meehl, et al. 2004; Hansen et al. 2005; Wild et al. 2007; Lockwood and Frohlich 2007, 2008; Lean and Rind 2008; Foster and Rahmstorf 2011; Imbers et al. 2014). However, including our greenhouse gases in the calculations does explain the warming trend ( Stott et al. 2001; Meehl et al. 2004; Allen et al. 2006; Lean and Rind 2008; Imbers et al. 2014). In other words, we know that our emissions are driving the warming, because we have tested the natural drivers of climate change, and none of them (even when combined) can account for the current increase in temperatures. Further, we know that CO2 traps heat and is largely responsible for both our climate and past climate changes ( Lorius et al. 1990; Tripati et al. 2009; Shakun et al. 2012), we know that we have greatly increased the CO2 in the atmosphere ( Bohm et al. 2002; Ghosh and Brand 2003; Wei et al. 2009), and we have used satellites to directly measure the amount of heat energy that CO2 in our atmosphere is trapping, and (just as expected) it is trapping an increasing amount of heat ( Harries et al. 2001; Griggs and Harries 2007). In other words, we have direct evidence that our CO2 is causing the earth to trap more heat (i.e., warm). The evidence is overwhelming, and when you look at the scientific literature, you will find thousands of papers saying that we are causing climate change, and only a tiny handful that disagree. The scientific consensus on this topic is incredible (details here), and the consensus among scientists exists because of the evidence. Scientists do not "believe" in climate change (as Scaramucci erroneously asserts), rather we accept it as fact because of the overwhelming evidence. There is simply no serious debate on this topic among actual scientists.

To illustrate this a different way, a few weeks ago, I attended the annual meeting of the Ecological Society of Australia, and there were lots of talks about the effects that climate change is having and will have on plants and animals, but not one of those talks hedged their comments with statements like, "if climate change is real." Rather, all of them treated anthropogenic climate change as a scientific fact because that is what it is, and it is absolutely crazy to suggest that we should act as if it isn't settled just because some non-scientists over 2000 years ago thought that the ear th was flat! Further, the fact that a handful of scientists today disagree doesn't help that argument either because there will always be a few who disagree on any topic (this is an appeal to authority fallacy). Having an advanced degree does not guarantee that you know what you are talking about, and you can sadly find plenty of crackpots even among scientists (note: before you accuse me of committing an appeal to authority fallacy by citing the consensus, please read this post on the difference between appealing to authority and deferring to experts).

To be clear, there is disagreement about the extent of climate change that we are going to cause. There are lots of variables that have to be considered, and there are admittedly still topics that we are actively studying in order to refine out models. However, although the models may be imprecise about the extent of the warming, it is almost unthinkable that we are wrong about the warming itself. Indeed, the idea that we are causing the climate to change is not based on the predictive models, rather it is based on research like the papers I presented earlier. Further, out models have actually done a pretty good job of predicting the current warming, so although they aren't perfect, they probably aren't that far off ( Hansen et al. 2006; Frame and Stone 2012; Rahmstorf et al. 2012; Cowtan et al. 2015; Marotzke and Firster 2015).

In short, the science that said that the earth is flat or that we are the center of the universe was not the same thing as what we know as science today. Further, the past few decades have furnished us with a fantastic array of amazing tools for collecting and analyzing massive amounts of data, and when we apply those tools to the topic of climate change, we get an extremely consistent result: it is real and we are the main cause of it. Because of that evidence, there is no serious debate about this among scientists. Yes, there are a few dissenting voices, but those will always be there. Further, yes, it is always technically possible that we are wrong, but it is also technically possible that Donald Trump is a space squid, and the fact that something is technically possible does not mean that it is rational to think that it is actually true.

* Note: When I said that science uses controlled experiments, I was not referring specifically to randomized controlled trials (RCTs). Although they are a particularly powerful design, they are far from the only one, and they often aren't applicable in fields outside of medicine. In climate research, for example, we generally can't do RCTs, but we can still control our experiments and analyses by measuring all of the confounding factors and including those factors in out analyses.
Related posts Literature cited

Allen et al. 2006. Quantifying anthropogenic influence on recent near-surface temperature change. Surveys in Geophysics 27:491-544.

Bohm et al. 2002. Evidence for preindustrial variations in the marine surface water carbonate system from coralline sponges. Geochemistry, Geophysics, Geosystems 3:1-13.

Cowtan et al. 2015. Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures. Geophysical Research Letters 42:6526-6534.

Foster and Rahmstorf 2011. Global temperature evolution 1979-2010. Environmental Research Letters 7:011002.

Frame and Stone 2012. Assessment of the first consensus prediction on climate change. Nature Climate Change 3:357-359.

Ghosh and Brand. 2003. Stable isotope ratio mass spectrometry in global climate change research. International Journal of Mass Spectrometry 228:1-33.

Griggs and Harries. 2007. Comparison of spectrally resolved outgoing longwave radiation over the tropical Pacific between 1970 and 2003 Using IRIS, IMG, and AIRS. Journal of Climate 20:3982-4001.

Hansen et al. 2005. Earth's energy imbalance: confirmation and implications. Science 308:1431.

Hansen et al. 2006. Global temperature change. Proceedings of the National Academy of Sciences 10314288-14293.

Harries et al. 2001. Increases in greenhouse forcing inferred from the outgoing longwave radiation spectra of the Earth in 1970 and 1997. Nature 410:355-357.

Imbers et al. 2014. Sensitivity of climate change detection and attribution to the characterization of internal climate variability. Journal of Climate 27:3477-3491.

Lean and Rind. 2008. How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophysical Research Letters 35:L18701.

Lockwood and Frohlich. 2007. Recently oppositely directed trends in solar climate forcings and the global mean surface air temperature. Proceedings of the National Academy of Sciences 463:2447-2460.

Lockwood and Frohlich. 2008. Recently oppositely directed trends in solar climate forcings and the global mean surface air temperature. II. Different reconstructions of the total solar irradiance variation and dependence on response time scale. Proceedings of the National Academy of Sciences 464:1367-1385.
Lorius et al. 1990. The ice-core record: climate sensitivity and future greenhouse warming. Nature 139-145.

Marotzke and Firster 2015. Forcing, feedback and internal variability in global temperature trends. Nature 517:565-570.

Meehl, et al. 2004. Combinations of natural and anthropogenic forcings in the twentieth-century climate. Journal of Climate 17:3721-3727.

Rahmstorf et al. 2012. Comparing climate projections to observations up to 2011. Environmental Research Letters 7:044035.

Shakun et al. 2012. Global warming preceded by increasing carbon dioxide

Stott et al. 2001. Attribution of twentieth century temperature change to natural and anthropogenic causes. Climate Dynamics17:1-21.

Tripati et al. 2009. Coupling CO2 and ice sheet stability over major climate transitions of the last 20 million years. Science 326:1394-1397.

Wei et al. 2009. Evidence for ocean acidification in the Great Barrier Reef of Australia. Geochimica et Cosmochimica Acta 73:2332-2346.

Wild et al. 2007. Impact of global dimming and brightening on global warming. Geophysical Research Letters

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Climate change and a flat earth (I can't believe we're still dealing with this argument)

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When I started this blog, I wanted it to be entirely about science and the fundamental concepts of logic. As such, I decided that topics like religion and politics where off limits. However, anytime that a politician makes a statement about science, they enter my territory and I feel obliged to deal with their comments...

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There are several different types of logic, but probably the two most common are deductive and inductive. Both of these play a vital role in science, but we use them for different purposes. Therefore, it is my intention to explain the differences between these types of logic and when and how we use both of them in science.

Deductive logic
This is the most powerful form of reasoning. It is the type of logic that results in logical proofs. It goes from general concepts and/or specific observations to a focused conclusion. For example,

1. The sum of the angles of any triangle equals 180 degrees (general concept)

2. Angle A of triangle ABC = 45 degrees (specific observation)

3. Angle B of triangle ABC = 90 degrees (specific observation)

4. Therefore angle C of triangle ABC = 45 degrees (focused conclusion).

Notice that the conclusion is a certainty. This is the power of deductive logic, it tells you what absolutely must be true (assuming that your premises are true).

To give another famous example:

1. All men are mortals (general concept)

2. Socrates is a man (specific observation)

3. Therefore, Socrates is a mortal (focused conclusion)

I used this example to bring up a very important point about deductive logic. Deductive syllogisms often include a premise that was arrived at via inductive logic, this will become important in the next section.

In science, deductive logic is typically what is used at to arrive at facts. In other words, we use it to determine the results of specific experiments. For example:

1. I stomach flushed turtle A

2. Its stomach contained the remains of a fish

3. Therefore, turtle A ate a fish

So in short, deductive logic always gives a specific, focused conclusion and is used in science to determine facts and the outcomes of individual experiments.
Inductive logic
In contrast to deductive logic, inductive logic always results in a general conclusion and can be used to construct theories. It should be noted, that it is impossible to use deductive logic to arrive at a theory. Theories only come from inductive logic.

Inductive logic works somewhat backwards from deductive logic. It starts with specific observations and works towards a general conclusion (note: both types of logic start with observations and work to a conclusion). For example, think back to the Socrates example in the deductive logic section. How do we know that all men are mortals? Well, we know that from inductive logic,

1. Every man that we have "tested" (observed) has been mortal (collection of specific observations)

2. There is no reason to think that an immortal man could exist (logical statement)

3. Therefore, all men are mortals (general conclusion)

Notice, an inductive conclusion is not as strong as a deductive conclusion, but it is still often very powerful. Technically speaking, it is true that I cannot be completely certain that there is not an immortal walking around pretending to be mortal, but there is simply no reason to think that such a person exists, so the conclusion is clearly valid. It should be noted, however, that not all inductive conclusions are equal. For example, if I said that, "I have liked every Christopher Nolen film so far, therefore, I like all Christopher Nolen films (present and future)" my conclusion is clearly dubious. There are so many variables involved in making a film that it is absurd to think that he will never make one that I don't like. This is generally not the case in science. In science, we use inductive logic with as few variables as possible, and we generally support our conclusions with mathematical models. Also, the consistency of the physical universe adds an extra level of support to our inductive conclusions.

Additionally, because of the law of large numbers, the strength of an inductive conclusion increases as the number of observations used to form the conclusion increases. If I measure the rate of something once, it would be absurd to say that it always moves at that rate. If I measure it 100 times, however, it becomes more certain. If I measure it 1,000 times, it becomes even more certain.

Perhaps the greatest support of an inductive conclusion is, however, its ability to predict other events/make things work. Suppose that I build a device that would only work if the aforementioned rate was constant, and, when I turn the device on, it works. That would be extremely strong evidence that my inductive conclusion was correct. In fact, predictive power is the benchmark that we use to measure the strength and validity of theories.

This brings me to the restatement of a very important point about all scientific theories. They all rely on inductive logic. This is inherent in the nature of a theory (i.e., it is a general framework based on observations and used to explain other observations), but, something only gets promoted to the status of theory after it has been shown to have a high predictive power. For example:

1. Every physical body with mass that we have tested has produced gravity and been acted upon by gravity.

2. There is no logical reason to think that gravity wouldn't be constant, and there are strong mathematical/logical reasons to think that gravity is a constant

3. Numerous functional devices and calculations rely on the concept that gravity is constant

4. Therefore all physical bodies with mass produce gravity and are acted upon by gravity (i.e., the universal theory of gravity).

Notice, technically, I cannot be 100% certain of the conclusion. I have not tested every physical body in the universe, but virtually everyone will agree that the conclusion is valid.

I explained in a previous post that laws are synonymous with theories, allow me to demonstrate this by showing that the second law of thermodynamics was also arrived at using inductive logic.

1. Every closed system that we have ever observed has increased in entropy

2. There is no logical reason to think that a closed system could decrease in entropy, and there are strong mathematical/logical reasons to think that entropy must always increase

3. Numerous devices/experiments only work because all closed systems increase in entropy

4. Therefore, all closed systems increase in entropy (i.e., the second law of thermodynamics)

To summarize, scientists generally use deductive logic to determine the outcomes of specific experiments (sometimes inductive logic is also required depending on the nature of the experiment), and we use inductive logic to generalize from those experiments and form laws and theories. This is true for all laws/theories, whether we are talking about the laws of thermodynamics or the theory of gravity.

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Using Deductive and Inductive Logic in Science

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There are several different types of logic, but probably the two most common are deductive and inductive. Both of these play a vital role in science, but we use them for different purposes. Therefore, it is my intention to explain the differences between these types of logic and when and how we use both of...

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Verification shows something to be true
Falsification shows something to be false.

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1. What, if any, statements does this passage contain?

2. If it’s an argument, what’s the conclusion? And what premises are supplied?

3. If it’s an argument, does it have any suppressed premises? If so, what are they?

4. How does the whole argument look in standard form with all premises made explicit?

5. Is the argument deductive or non-deductive?

6. If it’s deductive, is it valid? If it’s non-deductive, how strong is it? Can you think of a (plausible) counter-example?

7. Are the premises true?

8. So, is it a good or a bad argument?

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A premise is irrelevant if having it in the argument does not make it stronger and removing it from the argument does not make it weaker.

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For deductive arguments, a premise is relevant if removing it makes a valid argument invalid.

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Irrelevant premises do not make arguments bad by making them invalid. They make them bad (non-cogent) when they are false. So what should you do with irrelevant premises? Apply the principle of charity and remove them from the argument. Call this, pruning an argument.

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A cogent argument is a strong non-deductive argument that has true premises.

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A cogent argument is a strong non-deductive argument that has true premises.

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A cogent argument is by definition non-deductive, which means that the premises are intended to establish probable (but not conclusive) support for the conclusion.

A cogent argument is strong, so the premises, if they were true, would succeed in providing probable support for the conclusion.

The premises are actually true.

So the conclusion indeed receives probable support.

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A sound argument is a valid argument that has true premises.

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A sound argument is a valid argument that has true premises.

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A sound argument is a deductive argument. It’s trying to establish conclusive support for its conclusion.

The argument is valid: the premises, if true, would guarantee that the conclusion is also true.

The premises are actually true

Therefore, a sound argument guarantees that its conclusion is true.

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Counter-examples show invalidity / weakness of an argument

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Counter-examples show invalidity / weakness of an argument

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A counter-example to a deductive argument

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A counter-example to a deductive argument

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is a situation which shows that the argument can have true premises and a false conclusion.

If the argument being evaluated is deductive, then we can show it to be invalid and, therefore, bad if we can describe a counter-example.

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A counter-example to a non-deductive argument

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A counter-example to a non-deductive argument

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Since a non-deductive argument acknowledges that the conclusion might be false when the premises are true, but only in exceptional circumstances, you need to find a counter-example that is not so exceptional (a reasonable situation in which the premises are all true and the conclusion false).

If all you can come up is too far fetched, then you’re probably describing one of the exceptional cases that were excluded to begin with, and so you would fail to provide a counter-example, and you could conclude that the argument is strong.

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Implausible counter-examples are counter-examples all the same, but they can only show that a deductive argument is invalid. However, only a plausible counter-example can show that a non-deductive argument is weak.

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There is a possibility that the conclusion may be false.

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An argument is a group of statements some of which, the premises, are offered in support of another statement, the conclusion.

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A non-deductive argument that succeeds in providing probable, but not conclusive, logical support for its conclusion.

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An argument is simply a way to describe the way in which reasons support beliefs.

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An argument is simply a way to describe the way in which reasons support beliefs.

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How to evaluate an argument

1. Identify the conclusion and the premises.

2. Put the argument in standard form.

3. Decide if the argument is deductive or non-deductive.

4. Determine whether the argument succeeds logically.

5. If the argument succeeds logically, assess whether the premises are true. For premises that are backed-up by a sub-arguments, repeat all the steps for the sub-arguments.

6. Make a final judgement: is the argument good or bad?

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A deductive argument is an argument for which the premises are offered to provide logically conclusive support for its conclusion.

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1. Do the premises provide enough logical support for the conclusion?
2. Are the premises true?

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A deductive argument that succeeds in providing decisive logical support.

Is it possible for the premises to be true and the conclusion to be false?  (No)

An argument is valid if and only if there are no counter-examples to the argument, as it is then impossible to find a situation in which the premises of the argument are true and the conclusion is false.

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A non-deductive argument is an argument for which the premises are offered to provide probable – but not conclusive – support for its conclusions.

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Statements are the kind of sentences that are either true or false.

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Questions (except rhetorical)

Commands

Advice

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Conclusion indicators | Premise indicators

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Conclusion indicators | Premise indicators

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Therefore | Because
Thus | Since
Hence | Supposing that
Consequently | Assuming that
Ergo | Given that

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Confirmation bias - when we tend to only consider what we have already experienced.

Heuristics - mental shortcuts we use to simplify decision making.

Framing - when how we are presented with a problem affects the way we see it.

Common fallacies - some common ways that we use reasoning that are not logical or critical.

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Reasoning heuristic - A mental shortcut to simplify decision making is called
Availability heuristic - We judge the probability of an event by how quickly and easily examples of that event come to mind, by how available they are to us, rather than by identifying all of the alternatives and working out the real probabilities.

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“The human understanding, when it has once adopted an opinion draws all things … to support and agree with it. And though there be a greater number and weight of instances to be found on the other side, yet these it either neglects and despises or … sets aside and rejects in order that by this great and pernicious predetermination, the authority of its former conclusions may remain inviolate”.

(Francis Bacon 1602).

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Distinction between moral and scientific theories

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Distinction between moral and scientific theories

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Moral theories are not mere theories in the dismissive sense either. They too are based on repeated observations, are likely to integrate hypotheses, and attempt to explain and justify a range of moral or ethical judgements about particular cases.

But neither are moral theories quite like scientific theories. The data that scientific theories try to explain is provided by observation of the natural world. The data that moral theories try to explain is our considered moral judgements; judgements that have, we might say, survived the test of good logical and critical thinking. There is an obvious difference here. In the case of moral theories, we use our considered judgements to supply the data by which we judge the adequacy of moral theories.

Still, even with this difference acknowledged, we do use moral theories, and often in very similar ways to their scientific counterparts.

Moral theories offer general accounts of what makes an action right, accounting for as many of our considered judgments as possible (just as the scientists attempts to explain as many observations as possible), sometimes calling upon us to abandon some of those judgments, (just as scientists might reject some observations or hypotheses as mistaken), attempting to explain why a judgment in some past case is troubling and how we should judge in the future.

There is, however, one other important way in which moral theories are not like scientific theories:

In science, we assume that there is one right theory and that it will explain all of the data and probably lead us to abandon rival theories (though it may have incorporated significant parts of them). Moral theories do not seem to work quite like that. They are generated by our considered moral judgments and they reflect, for instance, the judgment that consequences matter; that recognition and respect for autonomous reasoning agents matter; that wisdom and judgment matter. Moral theories allow us to see the implications of the judgments that these (and other fundamental concerns) are each important in moral reasoning.

Moral theories are not ‘mere’ theories – untested, tentative, vague generalisations. They are based on repeated observations, integrated hypotheses, and attempt to explain a range of data. But the data that scientific theories try to explain is provided by observation of the natural world, the data that moral theories try to explain is our considered moral judgements; judgements that have, we might say, survived the test of good logical and critical thinking.

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A non-deductive argument that fails to provide probable support for its conclusion.

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is a claim that something is morally good or bad, right and wrong, or has some other moral quality, such as being just, admirable, or blameworthy.

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If the only counter-examples you can find are far-fetched stories, then you may have an indication to think that the argument is non-deductive.

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If the only counter-examples you can find are far-fetched stories, then you may have an indication to think that the argument is non-deductive.

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You might also think of the search for a counter-example as a method that helps you determine whether an argument is deductive or non-deductive. If finding a counter-example to an argument makes us want to say that argument is no good, then the argument must be deductive, because in deductive arguments the premises are intended to give conclusive support for the conclusion.

On the other hand, if generating a counter-example does not incline us to give up the argument, then it is a non-deductive argument because non-deductive arguments have conclusions which are only meant to be strongly suggested by their premises, and leave it open that the conclusion may be false.

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is an argument that includes at least one moral statement.