Inductive, deductive and Causal reasoning.pptx

reasoning is important to think critically for any managers

1. Reasoning: Inductive, Deductive and Causal - Prof Alisha Dhal

2. Tiny little things we see or read… Draw general principles All the swans I have seen are white. (Premise) Therefore all swans are white. (Conclusion)

3. But then… Now……

4. Inductive Reasoning Inductive: using a particular set of  facts or ideas  to form a  general principle. O 8 O 6 O 7 O 9 O 4 O 2 O 1 O 3 O 5 to make a general conclusion observations, such as observed patterns General Conclusion Strong reasons to believe…. Conclusion is true

5. Inductive Reasoning Starts with a set of  premises , based mainly on experience or experimental evidence  to generalize a  conclusion . Begins with an observation, is supported by patterns, and then leads to a hypothesis or theory I f the premises are true, the claim (conclusion) is likely to be true . It is important to note: inductive reasoning may still fail, if done well, can give good but less than 100% conclusive reason to believe the conclusion .

6. Inductive Reasoning

7. The conclusion is actually wrong— there are also black swans . Should we not make inductive reasoning?- Not really possible. However, we can make the argument stronger …  All the swans I have seen are white. (Premise) Therefore most swans are probably white. (Conclusion) BETTER CONCLUSION - KEEPING A SCOPE OF ERROR (WHICH IS POSSIBLE AS OBSERVATIONS BASIS WHICH WE MADE CONCLUSION MAY NOT BE 100% CORRECT Inductive Reasoning

8. Inductive Reasoning Induction always adds to the information we already know. Inductive  reasoning should always be tested to see if it is correct . How we use inductive reasoning: In everyday life  to build our understanding of the world. Scientific method : scientists gather data through observation and experiment, make hypotheses based on that data, and then test those theories further. Despite the potential for weak conclusions, an inductive argument is also the main type of reasoning in  academic life (We cannot test for 100% data but we are dependent on representative sample set).

9. Examples… The new plant I bought is a cactus. It didn’t thrive when I watered it a lot. Therefore, cactuses shouldn’t be watered too much. My car’s gas tank is empty. My car will not start. Therefore, cars don’t start when they’re out of gas. Every centaur I’ve met has been named Dave. Therefore, all centaurs must be named Dave.

10. Pros and Cons Big enough sample set, inductive reasoning can be highly accurate Enables us to model big phenomena that are impossible to directly measure Can lead to incorrect conclusions , especially when a dataset is too small Leads to stereotyping and untrue assumptions  about populations that have not been directly examined Encourages more investigation to see if the judgment or likely conclusion is correct or incorrect Multiple solutions and theories can be generated by using inductive reasoning.  

11. Lockdown help to slow the spread of virus by reducing person-to-person contact. Lockdowns can help prevent healthcare systems for becoming overwhelmed with patients, ensuring the ones need care receive it. Lockdowns can also buy time for medical professionals to develop effective treatment or vaccines for the virus. Lockdowns should be imposed during the pandemics such as COVID-19 Each of these premises independently support the claim. If you remove one or two premises the left premises will still justify the claim on its own strength. Convergent arguments MULTIPLE PREMISES TO SUPPORT THE CLAIM

12. ACTIVITY-1 (GROUP)

13. Deductive Reasoning From general (statement or rules) to specific . Conclusion (specific) doesn’t add any new information which is not already given in the premises (statement/rules). Conclusion in deduction “ must be true ” if “ premises are true ” A=B (Premise 1) B=C (Premise 2) Thus, A=C (Conclusion) Begins with a theory, is supported by observations, and eventually leads to confirmation. 

14. Deductive Reasoning If you begin with true premises and a valid argument , you’re bound to come to a true conclusion . (Premise-1) All fruits are grown from flowers and contain seeds (Premise-2) Tomatoes are grown from flowers and contain seeds. (Conclusion) Therefore, tomatoes are fruits. (Premise-1) If there’s a rainbow, flights get canceled. (Premise-2) There is a rainbow now. (Conclusion) Therefore, flights are cancelled. true premises leads to a true conclusion false premises leads to an invalid conclusion

15. Cactuses need only a limited amount of water. The new plant I’ve bought is a cactus. Therefore, I shouldn’t water the new plant too much. Gas-powered cars require gas in order to start. My car’s gas tank is empty. Therefore, my car will not start. All centaurs are half-human, half-horse. Dave is a centaur. Therefore, Dave is half-human, half horse. All fish live in water. Sharks are fish. Therefore, sharks live in water. Examples…

16. Sherlock deductions…. :/ Not deductions but abductions (special form of induction which is more explanatory and not just a mere statement)- most likely explanation for them—like taking the best guess But he is portrayed in the show doing “deductions” for audience to feel he is always (100%) correct---- (No!.. He may be 99.99% correct but there still a possibility that he is wrong because he is generalizing from the experiences he had/observed).

17. Pros and Cons Logical and precise. If our premises are true, then our conclusion must also be true. Scientists develop law and apply it in actual reality, by assuming that the law must be true. Deductive reasoning is limited by the accuracy of our premises. While deductive reasoning can be very precise, it can also be rigid and inflexible.  Teamwork, hiring employees, problem-solving, customer satisfaction practices require the use of deductive reasoning . When an individual believes something to be true because others are doing so, it can lead to a mistake. Group data does not always account for every individual’s choices in the world. 

18. ACTIVITY-2 (INDIVIDUAL)

19. ACTIVITY-3 (INDIVIDUAL)

20. Causal Reasoning Cause and Effect: essential in understanding various phenomenon. Cause: refers to anything responsible for bringing about an effect. Better team training with advanced skills Recruiting more sales staff Improving the performance incentive plan Higher sales CAUSES EFFECT

21. Types of cause and effect relationships Single cause- single effect (Ex: overheating of motor- broken fan belt). Multiple cause- single effect (Ex: too much cholesterol, lack of exercise and long stressful working hours led to cardiac arrest) Single cause-multiple effect (Ex: Rise in oil prices led to depreciation of rupee, higher inflation and slow GDP) Sequential cause and effect (Ex: virus multiplication leads to inflammation of organs which leads to damaging organs)

22. Correlation is not Causation Correlation and causation can seem deceptively similar but are not. Causation means one thing causes another—in other words, action A  causes  outcome B (one variable causes change in the other variable). On the other hand,  correlation is simply a relationship where action A  relates to  action B (statistical association) . .

23. Spurious correlations

24. Spurious correlations

25. When we do correlational studies? Commonly used when it’s unethical, too costly, or too difficult to perform controlled experiments. Ex: To study whether consuming violent media is related to aggression , you collect data on children’s video game use and their behavioral tendencies. You ask parents to report the number of weekly hours their child spent playing violent video games, and you survey parents and teachers on the children’s behaviors. You find a positive correlation between the variables: children who spend more time playing violent video games have higher rates of aggressive behavior.

26. Third variable problem Without controlled experiments, it’s hard to say whether it was the variable you’re interested in that caused changes in another variable. Role of extraneous or confounding variables: serve as alternative explanations for the results. Confounding variables can make it seem as though a correlational relationship is causal when it isn’t. Coffee drinkers Heart Disease More smoking

27. Medicine for diabetes 2 groups 1 group: medicine treatment …. 2 group: placebo- control (feel that they should get something) After one month: result in reduction in blood sugar levels Normal vs hybrid seeds in same area 2 groups Put plants (Everything else is same: Fertilizer, water, sunlight etc.) Only seeds = 1x2 Water + seeds = 2x2 Water + seeds + fertilizer = 2x2x2 Controlled Experiments

28. Example of a 2 X 2 Design   (IV) Seed type (IV) Water level (Level) Normal (Level) Hybrid (Level) Low Production (Kgs/acre) Production (Kgs/acre) (Level) High Production (Kgs/acre) Production (Kgs/acre)

29. Example of a 2 X 2 Design   (IV) Price promotion frame (IV) Discount depth (Level) Rs. (Level) Percentage (Level) Low Purchase likelihood Purchase likelihood (Level) High Purchase likelihood Purchase likelihood