This document summarizes key concepts related to reasoning, judgment, and decision making. It discusses:
- Deductive and inductive reasoning processes. Deductive reasoning uses known facts to make logical predictions, while inductive reasoning draws general conclusions based on specific observations.
- Common reasoning errors like belief bias, availability heuristic, and confirmation bias that can influence judgments.
- Factors that affect inductive arguments like representativeness of observations and number of observations.
- Conditional and categorical syllogisms as forms of deductive reasoning.
- Neurological evidence showing the prefrontal cortex is important for complex reasoning and problem solving.
- How framing, context, emotions
This document discusses three types of social science studies: case studies, cross-sectional studies, and longitudinal studies. It also outlines the basic steps of social science research: selecting variables of interest, proposing hypotheses about relationships between variables, and empirically testing those hypotheses. Additionally, it describes common mistakes in social research like contamination, hasty generalization, and incorrectly inferring relationships between variables at different levels of analysis.
Introduction to the Logic of social inquiryJohn Bradford
This document discusses key concepts in social science research methods. It outlines three types of social science studies: case studies which examine causes of events or conditions; cross-sectional studies which compare variables across different groups or places at the same time; and longitudinal studies which compare variables over time within the same group. The document then explains the steps to developing and testing social science explanations, including selecting variables of interest, developing hypotheses about relationships between variables, and empirically testing hypotheses while refuting alternative theories. Finally, it discusses some common mistakes in social research like contamination biases and logical fallacies.
Quiz show inductive & deductive reasoningclynnc
The document discusses the differences between deductive and inductive reasoning. It provides examples of statements using each type of reasoning. Deductive reasoning draws conclusions based on general rules or principles, while inductive reasoning draws conclusions based on observations or a collection of evidence, even if limited. However, both types of reasoning can lead to incorrect conclusions if not applied carefully. Stereotypes in particular are usually formed through inductive or deductive reasoning but are not always accurate.
This document discusses different types of logical fallacies. It begins by defining logic and fallacies. There are two types of fallacies discussed - formal fallacies which can be identified by examining the structure of an argument, and informal fallacies which require examining the content. Several examples of informal fallacies are then described in detail, including appeals to force/pity, red herrings, hasty generalizations, straw man arguments, appeals to inappropriate authority, the fallacies of composition and division.
This document provides an introduction to critical thinking and argumentation. It discusses key elements of arguments such as claims, grounds, warrants, backing, and qualifiers. It also introduces argument mapping as a tool to visualize the logical structure of arguments. Additionally, it discusses Stephen Toulmin's model of argument which identifies the core components of arguments and their interrelationships. The document uses examples to illustrate different types of arguments and their structures.
Reasoning and decision making involve cognitive processes such as deductive and inductive reasoning. Deductive reasoning uses syllogisms to reach definite conclusions from given premises, while inductive reasoning uses evidence to reach probable conclusions. Decision making involves choosing among alternatives and can be influenced by factors like risk aversion, emotional predictions, and how choices are framed. Brain regions like the prefrontal cortex are involved in higher-level cognitive functions used for reasoning and decision making.
Week 3 - Instructor Guidance
Week 3: Inductive Reasoning
This week’s guidance will cover the following topics:
1. The Nature of Inductive Reasoning
2. Appeals to Authority
3. Inductive Generalizations
4. Statistical Syllogisms
5. Arguments from Analogy
6. Inferences to the Best Explanation
7. Causal Reasoning
8. Things to Do This Week
The Nature of Inductive Reasoning
Will the sun rise tomorrow morning? Of course it will, but how do you know? The reasoning seems to go as follows:
Premise 1: The sun has risen every morning throughout known history
Conclusion: Therefore, the sun will rise tomorrow
Deductively, this argument is invalid, for it is logically possible that the earth could stop spinning tonight. Does that mean that the argument is no good? Of course not. In fact, its premise makes the conclusion is virtually certain. This is an example of a very good argument that is not intended to be deductively valid. That is because it is actually an inductive argument.
An argument is inductive if it does not attempt to be valid, but intends to give strong evidence for the truth of its conclusion.
Many might see inductive reasoning as inferior to deductive reasoning, but that is not generally the case. In fact, inductive arguments often provide much better arguments for the truths of their conclusions than deductive ones. The deductively valid version of our argument about the sun, for example, goes:
Premise 1: The sun will always rise in the morning
Conclusion: Therefore the sun will rise tomorrow morning
This second argument, while valid, actually gives less evidence for the conclusion because its second premise is false (the sun will eventually expand to engulf the earth and then collapse). Therefore the deductive argument is unsound and so offers little evidence for the conclusion, whereas the original inductive argument made the conclusion virtually certain. In other words, inductive reasoning in general can be even better than deductive reasoning in many cases; the trick is to determine which inductive arguments are good and which ones are not so good.Strength versus Weakness
Just as it is the goal of deductive reasoning to be valid, it is the goal of a inductive reasoning to be
strong
. An inductive argument is strong in case its premises, if true, would make the conclusion very likely to be true as well. The above argument about the sun rising is very strong. Most inductive arguments are less strong, all the way along a spectrum between strength and weakness. Here are three with varying degrees of inductive strength:
Weak:
Premise 1: John is tall and in college.
Conclusion: Therefore, he probably plays on the basketball team.
Moderate:
Premise 1: The Lions are a 14 point favorite.
Conclusion: So they will probably win.
Strong:
Premise 1: All of the TV meteorologists report a 99% chance of rain tomorrow.
Conclusion: So it will probably rain tomorrow.
Note that the degree of strength of an inductive argument is independent of whether the.
This document discusses three types of social science studies: case studies, cross-sectional studies, and longitudinal studies. It also outlines the basic steps of social science research: selecting variables of interest, proposing hypotheses about relationships between variables, and empirically testing those hypotheses. Additionally, it describes common mistakes in social research like contamination, hasty generalization, and incorrectly inferring relationships between variables at different levels of analysis.
Introduction to the Logic of social inquiryJohn Bradford
This document discusses key concepts in social science research methods. It outlines three types of social science studies: case studies which examine causes of events or conditions; cross-sectional studies which compare variables across different groups or places at the same time; and longitudinal studies which compare variables over time within the same group. The document then explains the steps to developing and testing social science explanations, including selecting variables of interest, developing hypotheses about relationships between variables, and empirically testing hypotheses while refuting alternative theories. Finally, it discusses some common mistakes in social research like contamination biases and logical fallacies.
Quiz show inductive & deductive reasoningclynnc
The document discusses the differences between deductive and inductive reasoning. It provides examples of statements using each type of reasoning. Deductive reasoning draws conclusions based on general rules or principles, while inductive reasoning draws conclusions based on observations or a collection of evidence, even if limited. However, both types of reasoning can lead to incorrect conclusions if not applied carefully. Stereotypes in particular are usually formed through inductive or deductive reasoning but are not always accurate.
This document discusses different types of logical fallacies. It begins by defining logic and fallacies. There are two types of fallacies discussed - formal fallacies which can be identified by examining the structure of an argument, and informal fallacies which require examining the content. Several examples of informal fallacies are then described in detail, including appeals to force/pity, red herrings, hasty generalizations, straw man arguments, appeals to inappropriate authority, the fallacies of composition and division.
This document provides an introduction to critical thinking and argumentation. It discusses key elements of arguments such as claims, grounds, warrants, backing, and qualifiers. It also introduces argument mapping as a tool to visualize the logical structure of arguments. Additionally, it discusses Stephen Toulmin's model of argument which identifies the core components of arguments and their interrelationships. The document uses examples to illustrate different types of arguments and their structures.
Reasoning and decision making involve cognitive processes such as deductive and inductive reasoning. Deductive reasoning uses syllogisms to reach definite conclusions from given premises, while inductive reasoning uses evidence to reach probable conclusions. Decision making involves choosing among alternatives and can be influenced by factors like risk aversion, emotional predictions, and how choices are framed. Brain regions like the prefrontal cortex are involved in higher-level cognitive functions used for reasoning and decision making.
Week 3 - Instructor Guidance
Week 3: Inductive Reasoning
This week’s guidance will cover the following topics:
1. The Nature of Inductive Reasoning
2. Appeals to Authority
3. Inductive Generalizations
4. Statistical Syllogisms
5. Arguments from Analogy
6. Inferences to the Best Explanation
7. Causal Reasoning
8. Things to Do This Week
The Nature of Inductive Reasoning
Will the sun rise tomorrow morning? Of course it will, but how do you know? The reasoning seems to go as follows:
Premise 1: The sun has risen every morning throughout known history
Conclusion: Therefore, the sun will rise tomorrow
Deductively, this argument is invalid, for it is logically possible that the earth could stop spinning tonight. Does that mean that the argument is no good? Of course not. In fact, its premise makes the conclusion is virtually certain. This is an example of a very good argument that is not intended to be deductively valid. That is because it is actually an inductive argument.
An argument is inductive if it does not attempt to be valid, but intends to give strong evidence for the truth of its conclusion.
Many might see inductive reasoning as inferior to deductive reasoning, but that is not generally the case. In fact, inductive arguments often provide much better arguments for the truths of their conclusions than deductive ones. The deductively valid version of our argument about the sun, for example, goes:
Premise 1: The sun will always rise in the morning
Conclusion: Therefore the sun will rise tomorrow morning
This second argument, while valid, actually gives less evidence for the conclusion because its second premise is false (the sun will eventually expand to engulf the earth and then collapse). Therefore the deductive argument is unsound and so offers little evidence for the conclusion, whereas the original inductive argument made the conclusion virtually certain. In other words, inductive reasoning in general can be even better than deductive reasoning in many cases; the trick is to determine which inductive arguments are good and which ones are not so good.Strength versus Weakness
Just as it is the goal of deductive reasoning to be valid, it is the goal of a inductive reasoning to be
strong
. An inductive argument is strong in case its premises, if true, would make the conclusion very likely to be true as well. The above argument about the sun rising is very strong. Most inductive arguments are less strong, all the way along a spectrum between strength and weakness. Here are three with varying degrees of inductive strength:
Weak:
Premise 1: John is tall and in college.
Conclusion: Therefore, he probably plays on the basketball team.
Moderate:
Premise 1: The Lions are a 14 point favorite.
Conclusion: So they will probably win.
Strong:
Premise 1: All of the TV meteorologists report a 99% chance of rain tomorrow.
Conclusion: So it will probably rain tomorrow.
Note that the degree of strength of an inductive argument is independent of whether the.
This document provides an overview of hypothesis testing concepts and analysis of variance techniques. It discusses hypothesis testing definitions and procedures, including forming the null and alternative hypotheses, identifying test statistics, computing p-values, and comparing p-values to significance values. It also describes different types of analysis of variance (ANOVA) models, including one-way ANOVA to compare multiple groups, multifactor ANOVA to analyze more than one categorical factor, variance components analysis to determine variability introduced at different levels, and general linear models that can handle both crossed and nested factors.
35818 Topic Discussion7Number of Pages 1 (Double Spaced).docxrhetttrevannion
- Emily scored one standard deviation above the mean on a standardized reading test.
- The normal curve can help understand this by showing that about 34% of children scored lower than Emily, and about 16% scored higher.
- A standard deviation above the mean means Emily scored higher than approximately 84% of other children who took the test.
35819 Topic Discussion8Number of Pages 1 (Double Spaced).docxrhetttrevannion
35819 Topic: Discussion8
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
I will upload the instruction
Discussion: Discuss, elaborate and give example.
Author: (Jackson, S. L. (2017). Statistics Plain and Simple, 4th Edition. Cengage Learning.)
Use this author as reference. I uploaded also the full text below.
Instructions:
Emily is a fifth-grade student who completed a standardized reading test. She scored one standard deviation above the mean score.
Answer the following questions:
· How does the normal curve help you understand what this means about how Emily compares to other children who took the test? Explain how you determined your findings.
· How many children scored lower than Emily?
· How many children scored higher?
Reference:
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal curve and probability.
In order to better understand the nature of probabilistic decisions, consider the following court.
The document discusses various study designs used in epidemiology. Descriptive studies involve systematically collecting and presenting data to describe a situation, such as case reports and case series. Analytical studies attempt to establish causes or risk factors by comparing groups with and without an outcome. Observational analytical studies include cross-sectional, case-control and cohort studies. Experimental studies randomly assign individuals to intervention and control groups to test interventions. Measures of association used include relative risk, odds ratio and attributable risk, which quantify the strength of relationships between exposures and outcomes.
35845 Topic Group AssignmentNumber of Pages 1 (Double Spaced.docxrhetttrevannion
35845 Topic: Group Assignment
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
Please follow the instruction carefully.
I will upload the instruction
Instruction: Please fill up or answer only the last topic on the Material I attach. Fill in directly to the material I provided.
Author: Jackson, S. L. (2017). Statistics plain and simple, (4th ed.). Boston, MA: Cengage Learning.
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal curve and probability.
In order to better understand the nature of probabilistic decisions, consider the following court case of The People v. Collins, 1968. In this case, the robbery victim was unable to identify his assailant. All that the victim could recall was that the assailant was female with a blonde pony tail. In addition, he remembered that she fled the scene in a yellow convertible that was driven by an African American male who had a full beard. The suspect in the case fit the.
35813 Topic Discussion2Number of Pages 1 (Double Spaced).docxrhetttrevannion
The document discusses probability and hypothesis testing concepts. It provides examples to illustrate key probability concepts like the multiplication rule and addition rule. The multiplication rule states that the probability of a series of independent events is the product of their individual probabilities. The addition rule states that the probability of mutually exclusive events is the sum of their individual probabilities. It also defines a null hypothesis as the default hypothesis that there is no relationship or no difference between groups. The alternative hypothesis is what would be supported if the null hypothesis is rejected.
35812 Topic discussion1Number of Pages 1 (Double Spaced).docxrhetttrevannion
35812 Topic: discussion1
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
I will upload the instruction
Reference: Probability and Hypothesis Testing. No running head please.
Author: (Jackson, S. L. (2017). Statistics plain and simple. (4th ed.). Boston, MA: Cengage Learning.) Please use this reference.
Question to be discuss: Discuss, elaborate and give example on the topic or question below.
****Define and share an example of a null hypothesis and an alternative hypothesis****
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal curve and probability.
In order to better understand the nature of probabilistic decisions, consider the following court case of The People v. Collins, 1968. In this case, the robbery victim was unable to identify his assailant. All that the victim could recall was that the assailant was female with a blonde pony tail. In addition, he remembered that she fled the scene in a yellow con.
This document discusses heuristics and biases in human judgment and decision-making. It provides examples of common heuristics like framing, anchoring, availability, and conjunction fallacy. It also discusses critiques of the heuristics and biases research from scholars like Gigerenzer, Lopes, and Hogarth. Gigerenzer argues that experiments are not representative and people can perform better with different framings. Lopes argues results are oversold and heuristics often give the right answer. Hogarth argues the research overlooks the dynamic nature of judgment and importance of feedback.
How to Write an Argumentative Essay Step By Step - Gudwriter. Sample Essay Outlines - 34+ Examples, Format, Pdf | Examples. Argumentative Essay Outline - 9+ Examples, Format, Pdf | Examples. A Sample Argumentative Essay.
- The document describes Stanley Milgram's famous experiment on obedience to authority from 1963. In the experiment, participants were instructed to administer electric shocks to a learner for incorrect answers, though no actual shocks were given.
- About 65% of participants administered what they believed were severe electric shocks, showing high obedience to authority. Each participant can be viewed as a Bernoulli trial with probability of 0.35 to refuse the shock.
- The document then discusses using the binomial distribution to calculate probabilities of outcomes with a given number of trials and probability of success for each trial. It provides the formula and conditions for applying the binomial distribution.
#35816 Topic Discussion5Number of Pages 1 (Double Spaced)N.docxAASTHA76
#35816 Topic: Discussion5
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: ATTACHED
I will upload the instruction
Discussion: Discuss, elaborate and give example. Please follow the instruction carefully. No running head please.
Author: (Jackson, S.L. (2017) Statistics Plain and Simple: (4th edition) - Cengage Learning)
Please use the author or refence that I provided
Instructions:
Review this week’s course materials and learning activities, and reflect on your learning so far this week. Respond to one or more of the following prompts in one to two paragraphs:
1. Provide citation and reference to the material(s) you discuss. Describe what you found interesting regarding this topic, and why.
2. Describe how you will apply that learning in your daily life, including your work life.
3. Describe what may be unclear to you, and what you would like to learn.
Reference:
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal c.
The document discusses different types of reasoning and logical fallacies. It begins by defining deductive reasoning, which uses facts and rules to arrive at a conclusion, and inductive reasoning, which uses patterns to arrive at a conjecture. Examples of each are provided. Common fallacies are also explained, including fallacies of relevance where the argument is irrelevant, causal fallacies where the cause does not make logical sense, false generalizations from insufficient evidence, and fallacies of ambiguity from equivocal language. Overall, the document provides an overview of deductive and inductive reasoning as well as common logical fallacies that can undermine arguments.
Many decisions are based on beliefs concerning the likelihoo.docxalfredacavx97
Many decisions are based on beliefs
concerning the likelihood of uncertain
events such as the outcome of an elec-
tion, the guilt of a defendant, or the
future value of the dollar. These beliefs
are usually expressed in statements such
as "I think that . .. ," "chances are
. . .," "it is unlikely that . .. ," and
so forth. Occasionally, beliefs concern-
ing uncertain events are expressed in
numerical form as odds or subjective
probabilities. What determines such be-
liefs? How do people assess the prob-
ability of an uncertain event or the
value of an uncertain quantity? This
article shows that people rely on a
limited number of heuristic principles
which reduce the complex tasks of as-
sessing probabilities and predicting val-
ues to simpler judgmental operations.
In general, these heuristics are quite
useful, but sometimes they lead to severe
and systematic errors.
The subjective assessment of proba-
bility resembles the subjective assess-
ment of physical quantities such as
distance or size. These judgments are
all based on data of limited validity,
which are processed according to heu-
ristic rules. For example, the apparent
distance of an object is determined in
part by its clarity. The more sharply the
object is seen, the closer it appears to
be. This rule has some validity, because
in any given scene the more distant
objects are seen less sharply than nearer
objects. However, the reliance on this
rule leads to systematic errors in the
estimation of distance. Specifically, dis-
tances are often overestimated when
visibility is poor because the contours
of objects are blurred. On the other
hand, distances are often underesti-
mated when visibility is good because
the objects are seen sharply. Thus, the
reliance on clarity as an indication of
distance leads to common biases. Such
biases are also found in the intuitive
judgment of probability. This article
describes three heuristics that are em-
ployed to assess probabilities and to
predict values. Biases to which these
heuristics lead are enumerated, and the
applied and theoretical implications of
these observations are discussed.
Representativeness
Many of the probabilistic questions
with which people are concerned belong
to one of the following types: What is
the probability that object A belongs to
class B? What is the probability that
event A originates from process B?
What is the probability that process B
will generate event A? In answering
such questions, people typically rely on
the representativeness heuristic, in
which probabilities are evaluated by the
degree to which A is representative of
B, that is, by the degree to which A
resembles B. For example, when A is
highly representative of B, the proba-
bility that A originates from B is judged
to be high. On the other hand, if A is
not similar to B, the probability that A
originates from B is judged to be low.
For an illustration of judgment b.
This document provides an introduction to critical thinking and the differences between deductive and inductive reasoning. It defines logic and reasoning, and explains that deductive reasoning moves from general premises to specific conclusions, while inductive reasoning moves from specific observations to broader generalizations. Examples of each type of reasoning are provided. The key differences are that deductive reasoning establishes absolute truths if the premises are true, while inductive reasoning reaches tentative conclusions.
This document discusses key concepts in social science research methods. It covers research ethics like informed consent and protecting vulnerable populations. It explains that good research should be valid, reliable and generalizable. It discusses the differences between quantitative and qualitative research methods. It also covers the deductive and inductive approaches to research, and explains the difference between correlation and causation in research. Key variables like independent and dependent variables are defined. The importance of hypotheses and how they relate to the research question or theory is also outlined.
Princiiples of Scientific Method in AnthropologyPaulVMcDowell
The document discusses principles of scientific method and anthropological method. It outlines tenets of anthropological research like cultural relativism versus ethnocentrism. It then discusses key concepts in scientific research like developing hypotheses, testing hypotheses, and replicating results. It also summarizes Lett's "Six-Way Test" for evaluating hypotheses, which includes falsifiability, logic, comprehensiveness, honesty, replicability, and sufficiency.
Princiiples of scientific method in anthropology-1198815173874496-3panaidu
The document discusses principles of scientific method and anthropological method. It covers concepts like cultural relativism, holism, cross-cultural comparison, and the importance of careful data gathering and logical reasoning. It also discusses developing hypotheses through induction and deduction, testing hypotheses, and challenges of replicating studies in anthropology due to cultural changes over time.
The document introduces several key concepts in psychology including intuition, common sense, psychological science, critical thinking, the scientific method, theories, hypotheses, research methods, experiments, and statistical analysis. It discusses how psychology aims to provide a scientific understanding of human behavior and mental processes through rigorous empirical study rather than relying solely on intuition or common sense.
This chapter discusses judgment and reasoning. It covers several topics:
- Judgment heuristics people use like availability and representativeness that can lead to biases.
- Detecting covariation between variables and neglecting base rates which impacts accuracy.
- Assessing evidence that confirms versus disconfirms one's beliefs and persevering in false beliefs.
- Different types of logical reasoning like syllogisms, conditionals, and Wason's selection task. People don't always use formal logic and make predictable errors.
- How decision making aims for utility maximization but is impacted by framing and risk preferences.
The document provides details of a 3-year strategic plan for a shoe manufacturer called G-Force Air. It summarizes G-Force Air's initial performance, competitors, and SWOT analysis. The 3-year plan projected increases in production, private label market share, and financial metrics like EPS and ROE. Actual performance met some targets but expansion plans were overly optimistic, leading to lower than projected metrics in years 17-18 due to insufficient production capacity. Key learnings included the importance of integrating functional areas and assessing risks realistically when crafting growth strategies.
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This document provides an overview of hypothesis testing concepts and analysis of variance techniques. It discusses hypothesis testing definitions and procedures, including forming the null and alternative hypotheses, identifying test statistics, computing p-values, and comparing p-values to significance values. It also describes different types of analysis of variance (ANOVA) models, including one-way ANOVA to compare multiple groups, multifactor ANOVA to analyze more than one categorical factor, variance components analysis to determine variability introduced at different levels, and general linear models that can handle both crossed and nested factors.
35818 Topic Discussion7Number of Pages 1 (Double Spaced).docxrhetttrevannion
- Emily scored one standard deviation above the mean on a standardized reading test.
- The normal curve can help understand this by showing that about 34% of children scored lower than Emily, and about 16% scored higher.
- A standard deviation above the mean means Emily scored higher than approximately 84% of other children who took the test.
35819 Topic Discussion8Number of Pages 1 (Double Spaced).docxrhetttrevannion
35819 Topic: Discussion8
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Writing Style: APA
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Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
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Discussion: Discuss, elaborate and give example.
Author: (Jackson, S. L. (2017). Statistics Plain and Simple, 4th Edition. Cengage Learning.)
Use this author as reference. I uploaded also the full text below.
Instructions:
Emily is a fifth-grade student who completed a standardized reading test. She scored one standard deviation above the mean score.
Answer the following questions:
· How does the normal curve help you understand what this means about how Emily compares to other children who took the test? Explain how you determined your findings.
· How many children scored lower than Emily?
· How many children scored higher?
Reference:
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal curve and probability.
In order to better understand the nature of probabilistic decisions, consider the following court.
The document discusses various study designs used in epidemiology. Descriptive studies involve systematically collecting and presenting data to describe a situation, such as case reports and case series. Analytical studies attempt to establish causes or risk factors by comparing groups with and without an outcome. Observational analytical studies include cross-sectional, case-control and cohort studies. Experimental studies randomly assign individuals to intervention and control groups to test interventions. Measures of association used include relative risk, odds ratio and attributable risk, which quantify the strength of relationships between exposures and outcomes.
35845 Topic Group AssignmentNumber of Pages 1 (Double Spaced.docxrhetttrevannion
35845 Topic: Group Assignment
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
Please follow the instruction carefully.
I will upload the instruction
Instruction: Please fill up or answer only the last topic on the Material I attach. Fill in directly to the material I provided.
Author: Jackson, S. L. (2017). Statistics plain and simple, (4th ed.). Boston, MA: Cengage Learning.
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal curve and probability.
In order to better understand the nature of probabilistic decisions, consider the following court case of The People v. Collins, 1968. In this case, the robbery victim was unable to identify his assailant. All that the victim could recall was that the assailant was female with a blonde pony tail. In addition, he remembered that she fled the scene in a yellow convertible that was driven by an African American male who had a full beard. The suspect in the case fit the.
35813 Topic Discussion2Number of Pages 1 (Double Spaced).docxrhetttrevannion
The document discusses probability and hypothesis testing concepts. It provides examples to illustrate key probability concepts like the multiplication rule and addition rule. The multiplication rule states that the probability of a series of independent events is the product of their individual probabilities. The addition rule states that the probability of mutually exclusive events is the sum of their individual probabilities. It also defines a null hypothesis as the default hypothesis that there is no relationship or no difference between groups. The alternative hypothesis is what would be supported if the null hypothesis is rejected.
35812 Topic discussion1Number of Pages 1 (Double Spaced).docxrhetttrevannion
35812 Topic: discussion1
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: Attached
I will upload the instruction
Reference: Probability and Hypothesis Testing. No running head please.
Author: (Jackson, S. L. (2017). Statistics plain and simple. (4th ed.). Boston, MA: Cengage Learning.) Please use this reference.
Question to be discuss: Discuss, elaborate and give example on the topic or question below.
****Define and share an example of a null hypothesis and an alternative hypothesis****
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal curve and probability.
In order to better understand the nature of probabilistic decisions, consider the following court case of The People v. Collins, 1968. In this case, the robbery victim was unable to identify his assailant. All that the victim could recall was that the assailant was female with a blonde pony tail. In addition, he remembered that she fled the scene in a yellow con.
This document discusses heuristics and biases in human judgment and decision-making. It provides examples of common heuristics like framing, anchoring, availability, and conjunction fallacy. It also discusses critiques of the heuristics and biases research from scholars like Gigerenzer, Lopes, and Hogarth. Gigerenzer argues that experiments are not representative and people can perform better with different framings. Lopes argues results are oversold and heuristics often give the right answer. Hogarth argues the research overlooks the dynamic nature of judgment and importance of feedback.
How to Write an Argumentative Essay Step By Step - Gudwriter. Sample Essay Outlines - 34+ Examples, Format, Pdf | Examples. Argumentative Essay Outline - 9+ Examples, Format, Pdf | Examples. A Sample Argumentative Essay.
- The document describes Stanley Milgram's famous experiment on obedience to authority from 1963. In the experiment, participants were instructed to administer electric shocks to a learner for incorrect answers, though no actual shocks were given.
- About 65% of participants administered what they believed were severe electric shocks, showing high obedience to authority. Each participant can be viewed as a Bernoulli trial with probability of 0.35 to refuse the shock.
- The document then discusses using the binomial distribution to calculate probabilities of outcomes with a given number of trials and probability of success for each trial. It provides the formula and conditions for applying the binomial distribution.
#35816 Topic Discussion5Number of Pages 1 (Double Spaced)N.docxAASTHA76
#35816 Topic: Discussion5
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
Language Style: English (U.S.)
Order Instructions: ATTACHED
I will upload the instruction
Discussion: Discuss, elaborate and give example. Please follow the instruction carefully. No running head please.
Author: (Jackson, S.L. (2017) Statistics Plain and Simple: (4th edition) - Cengage Learning)
Please use the author or refence that I provided
Instructions:
Review this week’s course materials and learning activities, and reflect on your learning so far this week. Respond to one or more of the following prompts in one to two paragraphs:
1. Provide citation and reference to the material(s) you discuss. Describe what you found interesting regarding this topic, and why.
2. Describe how you will apply that learning in your daily life, including your work life.
3. Describe what may be unclear to you, and what you would like to learn.
Reference:
Basic Probability Concepts
The Rules of Probability
Probability and the Standard Normal Distribution
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Module 8: Hypothesis Testing and Inferential Statistics
Null and Alternative Hypotheses
Two-Tailed and One-Tailed Hypothesis Tests
Type I and Type II Errors in Hypothesis Testing
Probability, Statistical Significance, and Errors
Using Inferential Statistics
Review of Key Terms
Module Exercises
Critical Thinking Check Answers
Chapter 4 Summary and Review
In this chapter you will be introduced to the concepts of probability and hypothesis testing. Probability is the study of likelihood and uncertainty. Most decisions that we make are probabilistic in nature. Thus, probability plays a critical role in most professions and in our everyday decisions. We will discuss basic probability concepts along with how to compute probabilities and the use of the standard normal curve in making probabilistic decisions.
probability The study of likelihood and uncertainty; the number of ways a particular outcome can occur, divided by the total number of outcomes.
Hypothesis testing is the process of determining whether a hypothesis is supported by the results of a research project. Our introduction to hypothesis testing will include a discussion of the null and alternative hypotheses, Type I and Type II errors, and one- and two-tailed tests of hypotheses as well as an introduction to statistical significance and probability as they relate to inferential statistics.
hypothesis testing The process of determining whether a hypothesis is supported by the results of a research study.
MODULE 7
Probability
Learning Objectives
•Understand how probability is used in everyday life.
•Know how to compute a probability.
•Understand and be able to apply the multiplication rule.
•Understand and be able to apply the addition rule.
•Understand the relationship between the standard normal c.
The document discusses different types of reasoning and logical fallacies. It begins by defining deductive reasoning, which uses facts and rules to arrive at a conclusion, and inductive reasoning, which uses patterns to arrive at a conjecture. Examples of each are provided. Common fallacies are also explained, including fallacies of relevance where the argument is irrelevant, causal fallacies where the cause does not make logical sense, false generalizations from insufficient evidence, and fallacies of ambiguity from equivocal language. Overall, the document provides an overview of deductive and inductive reasoning as well as common logical fallacies that can undermine arguments.
Many decisions are based on beliefs concerning the likelihoo.docxalfredacavx97
Many decisions are based on beliefs
concerning the likelihood of uncertain
events such as the outcome of an elec-
tion, the guilt of a defendant, or the
future value of the dollar. These beliefs
are usually expressed in statements such
as "I think that . .. ," "chances are
. . .," "it is unlikely that . .. ," and
so forth. Occasionally, beliefs concern-
ing uncertain events are expressed in
numerical form as odds or subjective
probabilities. What determines such be-
liefs? How do people assess the prob-
ability of an uncertain event or the
value of an uncertain quantity? This
article shows that people rely on a
limited number of heuristic principles
which reduce the complex tasks of as-
sessing probabilities and predicting val-
ues to simpler judgmental operations.
In general, these heuristics are quite
useful, but sometimes they lead to severe
and systematic errors.
The subjective assessment of proba-
bility resembles the subjective assess-
ment of physical quantities such as
distance or size. These judgments are
all based on data of limited validity,
which are processed according to heu-
ristic rules. For example, the apparent
distance of an object is determined in
part by its clarity. The more sharply the
object is seen, the closer it appears to
be. This rule has some validity, because
in any given scene the more distant
objects are seen less sharply than nearer
objects. However, the reliance on this
rule leads to systematic errors in the
estimation of distance. Specifically, dis-
tances are often overestimated when
visibility is poor because the contours
of objects are blurred. On the other
hand, distances are often underesti-
mated when visibility is good because
the objects are seen sharply. Thus, the
reliance on clarity as an indication of
distance leads to common biases. Such
biases are also found in the intuitive
judgment of probability. This article
describes three heuristics that are em-
ployed to assess probabilities and to
predict values. Biases to which these
heuristics lead are enumerated, and the
applied and theoretical implications of
these observations are discussed.
Representativeness
Many of the probabilistic questions
with which people are concerned belong
to one of the following types: What is
the probability that object A belongs to
class B? What is the probability that
event A originates from process B?
What is the probability that process B
will generate event A? In answering
such questions, people typically rely on
the representativeness heuristic, in
which probabilities are evaluated by the
degree to which A is representative of
B, that is, by the degree to which A
resembles B. For example, when A is
highly representative of B, the proba-
bility that A originates from B is judged
to be high. On the other hand, if A is
not similar to B, the probability that A
originates from B is judged to be low.
For an illustration of judgment b.
This document provides an introduction to critical thinking and the differences between deductive and inductive reasoning. It defines logic and reasoning, and explains that deductive reasoning moves from general premises to specific conclusions, while inductive reasoning moves from specific observations to broader generalizations. Examples of each type of reasoning are provided. The key differences are that deductive reasoning establishes absolute truths if the premises are true, while inductive reasoning reaches tentative conclusions.
This document discusses key concepts in social science research methods. It covers research ethics like informed consent and protecting vulnerable populations. It explains that good research should be valid, reliable and generalizable. It discusses the differences between quantitative and qualitative research methods. It also covers the deductive and inductive approaches to research, and explains the difference between correlation and causation in research. Key variables like independent and dependent variables are defined. The importance of hypotheses and how they relate to the research question or theory is also outlined.
Princiiples of Scientific Method in AnthropologyPaulVMcDowell
The document discusses principles of scientific method and anthropological method. It outlines tenets of anthropological research like cultural relativism versus ethnocentrism. It then discusses key concepts in scientific research like developing hypotheses, testing hypotheses, and replicating results. It also summarizes Lett's "Six-Way Test" for evaluating hypotheses, which includes falsifiability, logic, comprehensiveness, honesty, replicability, and sufficiency.
Princiiples of scientific method in anthropology-1198815173874496-3panaidu
The document discusses principles of scientific method and anthropological method. It covers concepts like cultural relativism, holism, cross-cultural comparison, and the importance of careful data gathering and logical reasoning. It also discusses developing hypotheses through induction and deduction, testing hypotheses, and challenges of replicating studies in anthropology due to cultural changes over time.
The document introduces several key concepts in psychology including intuition, common sense, psychological science, critical thinking, the scientific method, theories, hypotheses, research methods, experiments, and statistical analysis. It discusses how psychology aims to provide a scientific understanding of human behavior and mental processes through rigorous empirical study rather than relying solely on intuition or common sense.
This chapter discusses judgment and reasoning. It covers several topics:
- Judgment heuristics people use like availability and representativeness that can lead to biases.
- Detecting covariation between variables and neglecting base rates which impacts accuracy.
- Assessing evidence that confirms versus disconfirms one's beliefs and persevering in false beliefs.
- Different types of logical reasoning like syllogisms, conditionals, and Wason's selection task. People don't always use formal logic and make predictable errors.
- How decision making aims for utility maximization but is impacted by framing and risk preferences.
Similar to PSY 341 Judgement, Decisions, Reasoning Notes Abyana (20)
The document provides details of a 3-year strategic plan for a shoe manufacturer called G-Force Air. It summarizes G-Force Air's initial performance, competitors, and SWOT analysis. The 3-year plan projected increases in production, private label market share, and financial metrics like EPS and ROE. Actual performance met some targets but expansion plans were overly optimistic, leading to lower than projected metrics in years 17-18 due to insufficient production capacity. Key learnings included the importance of integrating functional areas and assessing risks realistically when crafting growth strategies.
This study investigated perceived ethnic prejudice experienced by British South Asian gay men from within the LGBT community. Semi-structured interviews were conducted with 12 British South Asian gay men about their experiences and identity. Three key themes emerged: 1) ethnic otherization on the gay scene where participants felt judged based on their race; 2) perceived mechanisms of rejection including racism and statements excluding Asians; 3) double rejection where participants were rejected by both their ethnic and LGBT communities, threatening their identity and psychological well-being. The study provided insights into prejudice at the intersection of ethnicity and sexuality using an identity process theoretical framework.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
How to Setup Default Value for a Field in Odoo 17Celine George
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
1. 27
Chapter 12 Judgement, Decisions and Reasoning
Reasoning
Cognitive processes by which people start with information and come to conclusions
that go beyond that information
Basic Processes
Reasoning
o Deduction (Deductive Reasoning)
Reasoning when facts are known
Broad principles to make logical predictions about specific
cases.
E.g. Formal Logic
Judgment
o Induction
Reasoning under uncertainty, must go beyond facts
Drawing general conclusions based on specific observations
and evidence.
o Conclusions we reach are probably, but not definitely
true.
E.g. statistical
Deductive Reasoning
Basic form is = Syllogism
o Syllogism includes:
2 Broad statements: Premises
Third Statement: Conclusion
Syllogism is valid if conclusions follows logically from its two
premises
o Categorical Syllogism
Premises and conclusion describe the relation between two
categories by using all, no or some.
o E.g.
Syllogism 1 (Example of categorical syllogism) --- VALID
Premise 1: All birds are animals
Premise 2: All animals eat food.
Conclusion: Therefore, all birds eat food.
Syllogism 2 ----VALID even if conclusion is not true.
Premise 1: All birds are animals
Premise 2: All animals have 4 legs
Conclusion: Therefore, all birds have 4 legs
o Validity depends on the form of the syllogism
o Truth refers to the content of the premises
DO NOT CONFUSE VALIDITY WITH TRUTH
How well can people judge validity?
Evaluation
o Ask people if conclusion follows logically from premises
2. 28
Production
o Ask people to indicate what logically follows from premises
E.g.
o Syllogism 3---NOT VALID
Premise 1: All of the students are tired
Premise 2: Some tired people are irritable
Conclusion: Some of the students are irritable
The conclusion DOES NOT follow from the two premises
o Syllogism not valid.
o Syllogism 4---NOT VALID
Illustrates the conclusion does not logically follow from the two
premises
Premise 1: All of the students live in Buffalo
Premise 2: Some people who live in Buffalo are millionaires
Conclusion: Therefore, some of the students are millionaires
Many errors in evaluation
o Belief Bias
be judged as valid.
Conditional Syllogisms
Have 2 premises and a conclusion ( as in categorical syllogisms)
But the first premise has the form
o
Common in everyday life
o Antecedent: If I
o Consequent: then I will get a good grade.
Example
Syllogism (Abstract version)
o Premise 1: If p, then q.
o Premise 2: p
o Conclusion: Therefore, q.
Syllogism 1(Affirming the antecedent) ---VALID,
97% judged correctly
o Premise 1: If I study, then I will get a good grade.
o Premise 2: I studied.
o Conclusion: Therefore, I will get a good grade.
Syllogism 2 (Denying the consequent) --- VALID
60% Judged correctly
o Premise 1: If I study, then I will get a good grade.
o Premise 2: I did not get a good grade
o Conclusion: Therefore, I did not study.
Syllogism 3 (Affirming the consequent) --- NOT VALID
40% Judged correctly
o Premise 1: If I study, then I will get a good grade.
o Premise 2: I got a good grade
o Conclusion: Therefore, I studied
3. 29
Syllogism 4 (Denying the antecedent) --- NOT VALID
40% Judged correctly
o Premise 1: If I study, then I will get a good grade.
o Premise 2: I did not study
o Conclusion: Therefore, I did not get a good grade.
Conditional Reasoning
o The Wason Four-Card Problem (Wason, 1966)
Indicate which cards you need to turn over to test the following rule
If there is a vowel on one side, then there is an even number
on the other side.
o E
o K
o 4
o 7
o How should we test a hypothesis?
Confirmatory Evidence
Evidence that is consistent with a hypothesis
Not informative because it is ambiguous
o There exist alternative explanations
Disconfirmatory Evidence (Falsification Principle)
Evidence that is inconsistent with a hypothesis
o Informative because it leads to tests that rule out
alternative explanations
o Crucial in scientific reasoning.
Falsification Principle
To test a rule, it is necessary to look for situations that would
falsify the rule.
o Most participants fail to do this
Wason Four card Problem (In real world
context)
Imagine you are a police officer who is
applying the rule:
o If a person is drinking beer, then
he or she must be over 19 years
old.
Beer
Soda
16 years old
24 years old
When problem is stated in concrete
everyday terms, correct responses
greatly increased.
T
Task
o Pragmatic reasoning schema
A way of thinking about
cause and effect in the
4. 30
world that is learned as
part of experiencing
everyday life
o Permission schema
If A is satisfied, B can be
carried out
People are familiar with
rules
If a person is drinking
beer, then they must be
over 19 years old.
Evolutionary Perspective on Cognition
Evolutionary principles of natural selection
o Wason task governed by a built-in cognitive program
for detecting cheating
The ability for 2 people to cooperate in a way
that is beneficial to both people
One proposed alternative to a
permission schema
o Conditional Reasoning Summary
Context is important
Familiarity is not always necessary for conditional reasoning
The controversy continues
Permission schema vs Detecting Cheating
Inductive Reasoning
Process of making general conclusions based on specific observations and evidences.
o Premises are based on observation of one or more specific cases
We generalize from these cases to more general conclusions with
varying degrees of certainty.
o A number of factors can contribute to the strength of an inductive argument
Representativeness of Observations
How well do the observations about a particular category
represent all of the members of that category?
o Observation:
All the crows I have seen in Pittsburgh are black.
When I visited my brother in Washington, DC,
the crows I saw there were black too.
o Conclusion:
I think it is a pretty good bet that all crows are
black
Number of Observations
o Adding more observations strengthen an inductive
argument
The argument about the crows is made stronger
by adding the Washington, DC observations to
the Pittsburgh observations
5. 31
others in the world are gray.
Quality of the Evidence
o Stronger evidence Stronger conclusions
Observation:
Here in New York, the sun has risen
every morning
Conclusion:
The sun is going to rise in New York
tomorrow.
The argument will become even stronger when
we consider scientific descriptions of how the
earth rotates on its axis and revolves around
the sun.
Inductive Reasoning
o Used to make scientific discoveries
Form hypotheses, test the hypotheses, and draw general conclusions
o We often use inductive reasoning in everyday life.
Make a prediction about what will happen based on observation
about what has happened in the past.
Heuristics
o Provide us with shortcuts to draw general conclusions based on specific
observations and evidence
Availability heuristic
Events that are more easily remembered are judged as being
more probable than events that are less easily remembered.
o Some possible causes of death are listed in the next
slide in pairs.
o Within each pair, which cause of death do you consider
to be more likely for people in the U.S.?
Homicide vs Appendicitis
Auto train collision vs Drowning
Asthma vs Tornado (58% thought tornado)
Appendicitis vs Pregnancy (83% thought
pregnancy)
Participants were more likely to pick
causes that had been publicized by the
media
Illusory correlations
A correlation between two events appears to exist, but in
reality, there is no correlation or it is much weaker than it is
assumed to be.
o People in big city are rude
Image from media or personal experiences
Illusory correlation reinforces stereotypes.
Representativeness Heuristic
6. 32
The probability that A is a member of Class B can be
determined by how well the properties of A resembles the
properties we usually associate with Class B
o Use base rate information if it is all that is available.
Base rate
Relative proportion of different classes
in a population.
o In a group of 100 ppl, 70 lawyers
and 30 engineers. What is the
chance that if we pick one
person at random, the person
will be an engineer?
30% CHANCE
o Use descriptive information if available and disregard
base rate information
Jack is a 45-year-old man. He is married and has
4 children. He is generally conservative, careful
and ambitious. He shows no interest in political
and social issues and spends most of his free
time on his many hobbies, which include home
carpentry, sailing and mathematical puzzles.
What is the chance that Jack will be an
engineer?
o 70% lawyer
o 30% engineer
Adding this description caused
participants to greatly increase their
estimate of the chances that the
randomly picked person was an
engineer.
Conjunction Rule:
The probability of a conjunction of 2 events (A and B) cannot
be higher than the probability of the single constituents (A
alone or B alone)
o Linda bank teller instead of bank teller and is active in
the feminist movement. (85% picked 2nd
)
Feminist bank tellers are a subset of bank
tellers, it is always more likely that someone is a
bank teller than a feminist bank teller.
Law of large numbers
The larger the number of individuals that are randomly drawn
from a population, the more representative the resulting
group will be of the entire population
o Samples of a small numbers of individuals will be less
representative of the population.
Which hospital recorded days on which more
than 60% of the babies born were boys?
7. 33
Smaller hospital
o 56% said about the same
Failed to consider the law
of large numbers
Confirmation Bias
Our tendency to selectively look for information that conforms
to our hypothesis and to overlook information that argues
against it.
o Wason (1966,1968)
Told participants that series was generated by
using a rule
Try to discover the rule by proposing
number trios of your own
2,4,6
o Get feedback on whether series
follows rule.
Announce rule when you
think you have figured it
out.
Most people
Ask: Does 8,10,12 follow
the rule?
Confirmation Bias
o Few ask
Does 1,2,3 or 6,4,2 or
10,10,10 follow the rule?
Myside bias
Type of confirmation bias in which people generate and test
hypotheses in a way that is biased toward their own opinions
and attitudes.
o Lord and coworkers (1979)
Identified one group of participants in favor of capital punishment and
another group against it.
Had both of the groups read the same article
o Those in favor found the article convincing
o Those against found the article unconvincing.
Decision Making
Expected Utility Theory
o People are basically rational
o So if they have all the relevant information, they will make a decision that
results in the maximum expected utility.
Utility
o
Economists The goal of good decision making is to make choices
that result in the maximum monetary payoff.
Advantages for the utility approach
o Specific
8. 34
o
Problems for utility approach
o People do not always make decisions that result in the desired outcome
Example.
Denes-Raj and Epstein (1994)
o Receive $1 when you draw a red jelly bean from a bowl
consisting red and white jelly beans.
Bowl containing 1 red and 9 white
10% probability of choosing red
Bowl containing 7 red and 93 white
7% Probability of choosing red
o But many chose this bowl with
less favorable probability.
Emotions after decisions
o Expected emotions
Emotions that people predict they will feel for a particular outcome.
Heads/Tails $10 example.
o Decline bc chances are 50/50
o Immediate emotions
Emotions that are experienced at the time a decision is being made
Two types:
o Integral immediate emotions
Emotions associated with the act of making a
decision.
o Incidental immediate emotions
Emotions that are unrelated to the decision
E.g.
o
o Something that happened earlier
in the day
Context can affect decisions
o Two camera models on display (Simonson & Tversky, 1992)
$170 model
$240 model
Decisions were split about equally between the $170 and $240
model
o But if a third camera for $470 was also on display,
Participants were more likely to choose the
$240 option than the $170 option
Decisions can depend on how choices are presented
o E.g.
Take a decision about whether to become a potential organ donor.
Only 28% have actually granted permission by signing a donor
card.
o This signing of the card = Opt-in procedure
Opt-in procedure
It requires the person to take an active step.
9. 35
Opt-out procedure
E.g.
o Everyone is a potential organ donor unless they
request not to be.
France and Belgium use an opt-out procedure
Consent rate is more than 99%
Framing effect
o Decisions are influenced by how the choices are stated, or framed
Can highlight one aspect of situation
Saved: 72% chose Program A
Die: 78% chose Program D
Taking risks
Justification in Decision Making
o Decision making process often includes looking for justification so the person
can state a rationale for his or her decision
I studied hard and performed well in the final exams, So I will treat
myself with Haagen-Dazs ice cream
The Physiology of Thinking
Prefrontal Cortex (PFC)
o The PFC plays a central role in determining complex behaviors that are
involved in thinking
Interferes with ability to act in a flexible manner
Important for problem solving
o One symptom of PFC damage is a behavior called perseveration
o Perseveration
Patients have difficulty switching from one pattern of behavior to
another
o Important for reasoning, planning and making connections among different
parts of a problem/story
As reasoning problems become more complex
Larger areas of the PFC are activated.
o Experiment
10. 36
Patients with prefrontal cortex damage
Deductive reasoning task (Waltz et al 1999)
o Participants were presented with relationships such as
Sam is taller than Nate
Nate is taller than Roger
o Participants asked to arrange the name in order of the
o Participants (3 groups)
Patients with PFC damage
Patients with temporal lobe damage
Participants without brain damage.
All groups did well when the task was
easy
However, when the task was made more
difficult by scrambling the order of the
presentation
o The patients with PFC damage
performed poorly.
Neuro economics
o Combines research from the fields of psychology, neuroscience, and
economics
One outcome of this approach
Has identified areas of the brain that are activated as people
make decisions while playing economic games
o Sanfey et al. (2003)
Ultimatum game
Two people play
o One is designated as the proposer
o Other designated as the responder
Proposer
o Given a sum of money ($10) and makes an offer to the
responder as to how this money should be split
between them
Responder
o If responder accepts the offer, then money is split
according to proposal
o If responder denies the offer, neither player receives
anything
Either way, game is over once responder makes
any decision
Results:
o Responders often rejected low offers
o Responders were angry because they felt the offers
were unfair
o They were less angry with an unfair computer
$5 accept
$3 most accept
11. 37
$1/$2 reject
Sanfey et al (2003) measured brain activity in the responders as they
were making their decisions
The right anterior insula, an area located deep within the brain
between parietal and temporal lobes,
o Was activated for about 3 times more strongly when
responders rejected an offer than when they accepted
it.
This area of the brain is connected with negative emotional
states
o Pain
o Distress
o Hunger
o Anger
o Disgust
Also found that the Prefrontal cortex (PFC) was also activated
by the decision task
o But this activation was the same for offers that are
rejected and offers that are accepted.
The same activation level in the PFC for offers
that were rejected and offers that were
accepted.
Omission Bias
o The tendency to do nothing to avoid having to make a decision that could be
interpreted as causing harm
o
I will not take the vaccine, and I accept the 10% chance of dying from
this flu
I will take the vaccine, and I accept the 5% chance of dying from the
weaker flu in the vaccine.
Zikmund-Fisher et al (2006)
o 52% of the participants decided to do nothing
Even though statistically this doubled their
chances of dying.
The end!!!!