{"id":607,"date":"2025-05-14T18:47:38","date_gmt":"2025-05-14T18:47:38","guid":{"rendered":"https:\/\/pressbooks.ccconline.org\/ppsccom1150publicspeaking\/?post_type=chapter&p=607"},"modified":"2025-07-13T21:25:36","modified_gmt":"2025-07-13T21:25:36","slug":"inductive-reasoning","status":"publish","type":"chapter","link":"https:\/\/pressbooks.ccconline.org\/ppsccom1150publicspeaking\/chapter\/inductive-reasoning\/","title":{"raw":"Inductive Reasoning","rendered":"Inductive Reasoning"},"content":{"raw":"Inductive reasoning <\/strong>(also called \u201cinduction\u201d) is probably the form of reasoning we use on a more regular basis. Induction is sometimes referred to as \u201creasoning from example or specific instance,\u201d and indeed, that is a good description. It could also be referred to as \u201cbottom-up\u201d thinking. Inductive reasoning is sometimes called \u201cthe scientific method,\u201d although you don\u2019t have to be a scientist to use it, and use of the word \u201cscientific\u201d gives the impression it is always right and always precise, which it is not. In fact, we are just as likely to use inductive logic incorrectly or vaguely as we are to use it well.\r\n
\r\n\r\nInductive reasoning<\/strong>\r\n\r\na type of reasoning in which examples or specific instances are used to supply strong evidence for (though not absolute proof of) the truth of the conclusion; the scientific method\r\n\r\n<\/div>\r\nInductive reasoning happens when we look around at various happenings, objects, behavior, etc., and see patterns. From those patterns we develop conclusions. There are four types of inductive reasoning, based on different kinds of evidence and logical moves or jumps.\r\n
\r\n

Generalization<\/h2>\r\nGeneralization <\/strong>is a form of inductive reasoning that draws conclusions based on recurring patterns or repeated observations. Vocabulary.com (2016) goes one step further to state it is \u201cthe process of formulating general concepts by abstracting common properties of instances.\u201d To generalize, one must observe multiple instances and find common qualities or behaviors and then make a broad or universal statement about them. If every dog I see chases squirrels, then I would probably generalize that all dogs chase squirrels.<\/span>\r\n
\r\n\r\nGeneralization<\/strong>\r\n\r\na form of inductive reasoning that draws conclusions based on recurring patterns or repeated observations\r\n\r\n<\/div>\r\n<\/div>\r\nIf you go to a certain business and get bad service once, you may not like it. If you go back and get bad treatment again, you probably won\u2019t go back again because you have concluded: \u201cBusiness X always treats its customers badly.\u201d However, according to the laws of logic, you cannot really say that; you can only say, \u201cIn my experience, Business X treats its customers badly\u201d or more precisely, \u201chas treated me badly.\u201d Additionally, the word \u201cbadly\u201d is imprecise, so to be a valid conclusion to the generalization, badly should be replaced with \u201crudely,\u201d \u201cdishonestly,\u201d or \u201cdismissively.\u201d The two problems with generalization are over-generalizing (making too big an inductive leap, or jump, from the evidence to the conclusion) and generalizing without enough examples (hasty generalization, also seen in stereotyping).\r\n\r\nIn the example of the service at Business X, two examples are really not enough to conclude that \u201cBusiness X treats customers rudely.\u201d The conclusion does not pass the logic test for generalization, but pure logic may not influence whether or not you patronize the business again. Logic and personal choice overlap sometimes and separate sometimes. If the business is a restaurant, it could be that there is one particularly rude server at the restaurant, and he happened to wait on you during both of your experiences. It is possible that everyone else gets fantastic service, but your generalization was based on too small a sample.\r\n\r\nInductive reasoning through generalization is used in surveys and polls. If a polling organization follows scientific sampling procedures (sample size, ensuring different types of people are involved, etc.), it can conclude that their poll indicates trends in public opinion. Inductive reasoning is also used in science. We will see from the examples below that inductive reasoning does not result in certainty. Inductive conclusions are always open to further evidence, but they are the best conclusions we have now.\r\n\r\nFor example, if you are a coffee drinker, you might hear news reports at one time that coffee is bad for your health, and then six months later another study shows coffee has positive effects on your health. Scientific studies are often repeated or conducted in different ways to obtain more and better evidence and make updated conclusions. Consequently, the way to disprove inductive reasoning is to provide contradictory evidence or examples.\r\n

Causal reasoning<\/h2>\r\nInstead of looking for patterns the way generalization does, causal reasoning <\/strong>seeks to make cause-effect connections. Causal reasoning is a form of inductive reasoning we use all the time without even thinking about it. If the street is wet in the morning, you know that it rained based on past experience. Of course, there could be another cause\u2014the city decided to wash the streets early that morning\u2014but your first conclusion would be rain. Because causes and effects can be so multiple and complicated, two tests are used to judge whether the causal reasoning is valid.\r\n
\r\n\r\nCausal reasoning<\/strong>\r\n\r\na form of inductive reasoning that seeks to make cause-effect connections\r\n\r\n<\/div>\r\nGood inductive causal reasoning meets the tests of directness <\/em>and strength<\/em>. The alleged cause must have a direct <\/em>relationship on the effect and the cause must be strong enough to make the effect. If a student fails a test in a class that they studied for, they would need to examine the causes of the failure. They could look back over the experience and suggest the following reasons for the failure:\r\n
\r\n
    \r\n \t
  1. They waited too long to study.<\/li>\r\n \t
  2. They had incomplete notes.<\/li>\r\n \t
  3. They didn\u2019t read the textbook fully.<\/li>\r\n \t
  4. They wore a red hoodie when they took the test.<\/li>\r\n \t
  5. They ate pizza from Pizza Heaven the night before.<\/li>\r\n \t
  6. They only slept four hours the night before.<\/li>\r\n \t
  7. The instructor did not do a good job teaching the material.<\/li>\r\n \t
  8. They sat in a different seat to take the test.<\/li>\r\n \t
  9. Their favorite football team lost its game on the weekend before.<\/li>\r\n<\/ol>\r\n<\/div>\r\nWhich of these causes are direct enough and strong enough to affect his performance on the test? All of them might have had a slight effect on his emotional, physical, or mental state, but all are not strong enough to affect their knowledge of the material if they had studied sufficiently and had good notes to work from. Not having enough sleep could also affect their attention and processes more directly than, say, the pizza or football game. We often consider \u201ccauses\u201d such as the color of the hoodie to be superstitions (\u201cI had bad luck because a black cat crossed my path\u201d).\r\n\r\nTaking a test while sitting in a different seat from the one where you sit in class has actually been researched (Sauffley, Otaka, & Bavaresco, 1985), as has whether sitting in the front or back affects learning (Benedict & Hoag, 2004). (In both cases, the evidence so far says that they do not have an impact, but more research will probably be done.) From the list above, #1-3, #6, and #7 probably have the most direct effect on the test failure. At this point our student would need to face the psychological concept of locus of control, or responsibility\u2014was the failure on the test mostly their doing, or their instructor\u2019s?\r\n\r\nCausal reasoning is susceptible to four fallacies: historical fallacy, slippery slope, false cause, and confusing correlation and causation. The first three will be discussed later, but the last is very common, and if you take a psychology or sociology course, you will study correlation and causation well.\r\n
    \r\n

    The Danger of Mixing Up Causality and Correlation<\/p>\r\n\r\n<\/header>\r\n

    \r\n\r\nThere is a difference between causation and correlation. In social scientific studies, researchers hesitate to state that there is a direct cause as that cause cannot always be proven. However, a correlation is more likely and logically sound. Ionica Smeets explains this well in the following TedTalk.\r\n\r\n[embed]https:\/\/youtu.be\/8B271L3NtAw?si=ulczNAcJceR3PYiI[\/embed]\r\n\r\n<\/div>\r\n<\/div>\r\n

    Sign Reasoning<\/h2>\r\nRight now, as one of the authors is writing this chapter, the leaves on the trees are turning brown, the grass does not need to be cut every week, and geese are flying toward Florida. These are all signs of fall in this region. These signs do not make fall happen, and they don\u2019t make the other signs\u2014 cooler temperatures, for example\u2014happen. All the signs of fall are caused by one thing: the rotation of the earth and its tilt on its axis, which make shorter days, less sunshine, cooler temperatures, and less chlorophyll in the leaves, leading to red and brown colors.\r\n\r\nIt is easy to confuse signs and causes. Sign reasoning<\/strong>, then, is a form of inductive reasoning in which conclusions are drawn about phenomena based on events that precede or co-exist with, but not cause, a subsequent event. Signs are like the correlation mentioned above under causal reasoning. If someone argues, \u201cIn the summer more people eat ice cream, and in the summer there is statistically more crime. Therefore, eating more ice cream causes more crime!\u201d (or \u201cmore crime makes people eat more ice cream.\u201d), that, of course, would be silly. These are two things that happen at the same time\u2014signs\u2014but they are effects of something else \u2013 hot weather. If we see one sign, we will see the other. Either way, they are signs or perhaps two different things that just happen to be occurring at the same time, but not causes.\r\n
    \r\n\r\nSign reasoning<\/strong>\r\n\r\na form of inductive reasoning in which conclusions are drawn about phenomena based on events that precede or co-exist with (but not cause) a subsequent event\r\n\r\n<\/div>\r\n

    Analogical reasoning<\/h2>\r\nAs mentioned above, analogical reasoning <\/strong>involves comparison. For it to be valid, the two things (schools, states, countries, businesses) must be truly alike in many important ways\u2013essentially alike. Although Harvard University and your college are both institutions of higher education, they are not essentially alike in very many ways. They may have different missions, histories, governance, surrounding locations, sizes, clientele, stakeholders, funding sources, funding amounts, etc. So it would be foolish to argue, \u201cHarvard has a law school; therefore, since we are both colleges, my college should have a law school, too.\u201d On the other hand, there are colleges that are very similar to your college in all those ways, so comparisons could be valid in those cases.\r\n
    \r\n\r\nAnalogical reasoning<\/strong>\r\n\r\ndrawing conclusions about an object or phenomenon based on its similarities to something else\r\n\r\n<\/div>\r\nYou have probably heard the phrase, \u201cthat is like comparing apples and oranges.\u201d When you think about it, though, apples and oranges are more alike than they are different (they are both still fruit, after all). This observation points out the difficulty of analogical reasoning\u2014how similar do the two \u201cthings\u201d have to be for there to be a valid analogy? Second, what is the purpose of the analogy? Is it to prove that State College A has a specific program (sports, Greek societies, a theatre major), therefore, College B should have that program, too? Are there other factors to consider? Analogical reasoning is one of the less reliable forms of logic, although it is used frequently.\r\n\r\nTo summarize, inductive or bottom-up reasoning comes in four varieties, each capable of being used correctly or incorrectly. Remember that inductive reasoning is disproven by counter-evidence and its conclusions are always up to revision by new evidence\u2013what is called \u201ctentative,\u201d because the conclusions might have to be revised. Also, the conclusions of inductive reasoning should be precisely stated to reflect the evidence.","rendered":"

    Inductive reasoning <\/strong>(also called \u201cinduction\u201d) is probably the form of reasoning we use on a more regular basis. Induction is sometimes referred to as \u201creasoning from example or specific instance,\u201d and indeed, that is a good description. It could also be referred to as \u201cbottom-up\u201d thinking. Inductive reasoning is sometimes called \u201cthe scientific method,\u201d although you don\u2019t have to be a scientist to use it, and use of the word \u201cscientific\u201d gives the impression it is always right and always precise, which it is not. In fact, we are just as likely to use inductive logic incorrectly or vaguely as we are to use it well.<\/p>\n

    \n

    Inductive reasoning<\/strong><\/p>\n

    a type of reasoning in which examples or specific instances are used to supply strong evidence for (though not absolute proof of) the truth of the conclusion; the scientific method<\/p>\n<\/div>\n

    Inductive reasoning happens when we look around at various happenings, objects, behavior, etc., and see patterns. From those patterns we develop conclusions. There are four types of inductive reasoning, based on different kinds of evidence and logical moves or jumps.<\/p>\n

    \n

    Generalization<\/h2>\n

    Generalization <\/strong>is a form of inductive reasoning that draws conclusions based on recurring patterns or repeated observations. Vocabulary.com (2016) goes one step further to state it is \u201cthe process of formulating general concepts by abstracting common properties of instances.\u201d To generalize, one must observe multiple instances and find common qualities or behaviors and then make a broad or universal statement about them. If every dog I see chases squirrels, then I would probably generalize that all dogs chase squirrels.<\/span><\/p>\n

    \n

    Generalization<\/strong><\/p>\n

    a form of inductive reasoning that draws conclusions based on recurring patterns or repeated observations<\/p>\n<\/div>\n<\/div>\n

    If you go to a certain business and get bad service once, you may not like it. If you go back and get bad treatment again, you probably won\u2019t go back again because you have concluded: \u201cBusiness X always treats its customers badly.\u201d However, according to the laws of logic, you cannot really say that; you can only say, \u201cIn my experience, Business X treats its customers badly\u201d or more precisely, \u201chas treated me badly.\u201d Additionally, the word \u201cbadly\u201d is imprecise, so to be a valid conclusion to the generalization, badly should be replaced with \u201crudely,\u201d \u201cdishonestly,\u201d or \u201cdismissively.\u201d The two problems with generalization are over-generalizing (making too big an inductive leap, or jump, from the evidence to the conclusion) and generalizing without enough examples (hasty generalization, also seen in stereotyping).<\/p>\n

    In the example of the service at Business X, two examples are really not enough to conclude that \u201cBusiness X treats customers rudely.\u201d The conclusion does not pass the logic test for generalization, but pure logic may not influence whether or not you patronize the business again. Logic and personal choice overlap sometimes and separate sometimes. If the business is a restaurant, it could be that there is one particularly rude server at the restaurant, and he happened to wait on you during both of your experiences. It is possible that everyone else gets fantastic service, but your generalization was based on too small a sample.<\/p>\n

    Inductive reasoning through generalization is used in surveys and polls. If a polling organization follows scientific sampling procedures (sample size, ensuring different types of people are involved, etc.), it can conclude that their poll indicates trends in public opinion. Inductive reasoning is also used in science. We will see from the examples below that inductive reasoning does not result in certainty. Inductive conclusions are always open to further evidence, but they are the best conclusions we have now.<\/p>\n

    For example, if you are a coffee drinker, you might hear news reports at one time that coffee is bad for your health, and then six months later another study shows coffee has positive effects on your health. Scientific studies are often repeated or conducted in different ways to obtain more and better evidence and make updated conclusions. Consequently, the way to disprove inductive reasoning is to provide contradictory evidence or examples.<\/p>\n

    Causal reasoning<\/h2>\n

    Instead of looking for patterns the way generalization does, causal reasoning <\/strong>seeks to make cause-effect connections. Causal reasoning is a form of inductive reasoning we use all the time without even thinking about it. If the street is wet in the morning, you know that it rained based on past experience. Of course, there could be another cause\u2014the city decided to wash the streets early that morning\u2014but your first conclusion would be rain. Because causes and effects can be so multiple and complicated, two tests are used to judge whether the causal reasoning is valid.<\/p>\n

    \n

    Causal reasoning<\/strong><\/p>\n

    a form of inductive reasoning that seeks to make cause-effect connections<\/p>\n<\/div>\n

    Good inductive causal reasoning meets the tests of directness <\/em>and strength<\/em>. The alleged cause must have a direct <\/em>relationship on the effect and the cause must be strong enough to make the effect. If a student fails a test in a class that they studied for, they would need to examine the causes of the failure. They could look back over the experience and suggest the following reasons for the failure:<\/p>\n

    \n
      \n
    1. They waited too long to study.<\/li>\n
    2. They had incomplete notes.<\/li>\n
    3. They didn\u2019t read the textbook fully.<\/li>\n
    4. They wore a red hoodie when they took the test.<\/li>\n
    5. They ate pizza from Pizza Heaven the night before.<\/li>\n
    6. They only slept four hours the night before.<\/li>\n
    7. The instructor did not do a good job teaching the material.<\/li>\n
    8. They sat in a different seat to take the test.<\/li>\n
    9. Their favorite football team lost its game on the weekend before.<\/li>\n<\/ol>\n<\/div>\n

      Which of these causes are direct enough and strong enough to affect his performance on the test? All of them might have had a slight effect on his emotional, physical, or mental state, but all are not strong enough to affect their knowledge of the material if they had studied sufficiently and had good notes to work from. Not having enough sleep could also affect their attention and processes more directly than, say, the pizza or football game. We often consider \u201ccauses\u201d such as the color of the hoodie to be superstitions (\u201cI had bad luck because a black cat crossed my path\u201d).<\/p>\n

      Taking a test while sitting in a different seat from the one where you sit in class has actually been researched (Sauffley, Otaka, & Bavaresco, 1985), as has whether sitting in the front or back affects learning (Benedict & Hoag, 2004). (In both cases, the evidence so far says that they do not have an impact, but more research will probably be done.) From the list above, #1-3, #6, and #7 probably have the most direct effect on the test failure. At this point our student would need to face the psychological concept of locus of control, or responsibility\u2014was the failure on the test mostly their doing, or their instructor\u2019s?<\/p>\n

      Causal reasoning is susceptible to four fallacies: historical fallacy, slippery slope, false cause, and confusing correlation and causation. The first three will be discussed later, but the last is very common, and if you take a psychology or sociology course, you will study correlation and causation well.<\/p>\n

      \n
      \n

      The Danger of Mixing Up Causality and Correlation<\/p>\n<\/header>\n

      \n

      There is a difference between causation and correlation. In social scientific studies, researchers hesitate to state that there is a direct cause as that cause cannot always be proven. However, a correlation is more likely and logically sound. Ionica Smeets explains this well in the following TedTalk.<\/p>\n