PSY 179 Critical Thinking in Psychology - Correlation vs Causation PDF

26.11.2024 PSY 179- Critical thinking in Psychology Correlation vs. Causation Today Correlation vs. causation...

26.11.2024 PSY 179- Critical thinking in Psychology Correlation vs. Causation Today Correlation vs. causation 1  26.11.2024 Correlation and causation Understanding the relationship between variables Defining correlation: Correlation measures the relationship between two variables, showing how one variable changes when another does. Causation: Direct cause-effect relationship- When changes in one variable directly cause changes in another. Requires controlled testing to prove. Correlation vs. causation: Correlation does not imply causation. A correlation simply indicates a relationship, but not the direction of influence or existence of a cause. Why it matters?: Misunderstanding correlation can lead to incorrect conclusions, affecting areas like health, education, and public policy. 2  26.11.2024 Spurious correlation: A statistical relationship that seems to exist between two variables but is caused by an external third factor. What is a third variable?: A third variable can create a false impression of causality between two variables, leading to spurious correlations. Birth control by the toaster method An example of spurious correlation The toaster method: An infamous example in Taiwan, where owning a toaster was found to correlate with lower birth rates. However, the relationship was due to socioeconomic factors. Understanding the third variable: The third variable in this toaster example was wealth. Wealthier families had more appliances and fewer children. 3  26.11.2024 Another example: Ice cream sales and drowning A correlation exists between ice cream sales and drowning rates in summer Why? This correlation is driven by the third variable, hot whether. The impact on decision making: Failing to recognize spurious correlations can lead to faulty conclusions in policy and research. Partial correlation Controlling for third variables Partial correlation: A statistical method that measures the relationship between two variables while controlling for the influence of a third variable. 4  26.11.2024 Example: Crime rates and temperature Testing the heat hypothesis The heat hypothesis: This hypothesis suggests a positive correlation between high temperatures and increased violent crime rates. What can be controlled as a third variable? Factors like time of day and neighborhood conditions, season and other activities also influence crime rates. Importance in research: Partial correlation is a powerful tool for reducing confounding factors and getting closer to true causality in correlational studies. Real-world implications: Understanding environmental factors in crime can help inform public policy and policing strategies. 5  26.11.2024 Directionality problem Establishing cause in correlation The directionality issue: In correlations, it is difficult to determine which variable causes the other, leading to ambiguity in interpretation. Example: Does high self-esteem lead to better grades, or do better grades lead to high self-esteem? Resolving directionality: Experimental designs with manipulation of variables can help in determining the direction of causality. Reversing assumptions While many assume high self-esteem leads to academic success, research suggests the reverse is often true- achievement boosts self-esteem. Research implications: This shift in understanding affects educational strategies and how we support students’ academic development. 6  26.11.2024 Selection bias: How sampling affects results What is selection bias?: Selection bias occurs when the sample used in a study is not representative of the population, leading to skewed results. Examples in education research: Private school students may outperform public school students, but this difference may result from socioeconomic factors, not the schools themselves. Controlling for SES: Studies must account for socioeconomic factors to accurately compare school types. Avoiding selection bias: Random sampling and careful controls can help mitigate the effects of selection bias in studies. The WEIRD bias The WEIRD bias: Western, Educated, Industrialized, Rich, and Democratic societies dominate psychological research. How does this bias affect the universality of psychological findings? 7  26.11.2024 What is illusory correlation?: The human brain tends to see The illusion of patterns or relationships between unrelated events, leading to false conclusions. correlation How we see patterns Why it matters: Illusory correlations can reinforce that don’t exist stereotypes and pseudoscientific beliefs, distorting our understanding of the world. 8  26.11.2024 Illusion of control Misinterpreting randomness What is the illusion of control?: People often believe they can control outcomes in random events, such as gamblers thinking they can influence a roll of the dice. Coincidence vs. control: People confuse random chance with control, leading to superstitious beliefs and irrational behaviors. Psychological impact: The illusion of control can affect decision-making in areas like gambling and business strategies. Case studies and confirmation bias Limits of case study evidence Confirmation bias: The tendency to search for, interpret, and remember information that confirms one’s pre-existing beliefs. Case studies are not causation: Relying on case studies can lead to confirmation bias and cannot establish causal relationships. The role of scientific testing: Only through systematic testing can real causal links be identified, avoiding biased interpretations. 9  26.11.2024 Pseudoscience and correlation Misleading interpretations in popular beliefs Pseudoscience refers to beliefs or practices that claim to be scientific but lack empirical support for testing. Correlation misuse: Pseudoscience often misuse correlational data to make false claims, such as astrology or alternative medicine practices. The role of critical thinking: Critical thinking helps to identify when correlational data is being misinterpreted or misused in pseudoscientific claims. Real-world example: Hormone replacement therapy (HRT) for menopause The impact of misleading correlation Initial findings: Early correlational studies suggested that HRT was linked to reduced heart disease, influencing medical recommendations. The role of selection bias: Later studies found that the correlation was driven by selection bias, with healthier women are more likely to use HRT. Consequences of misinterpretation: This mis interpretation led to widespread changes in medical advice, later revised after randomized controlled trials. 10  26.11.2024 Technological correlations Misleading interpretations in corporate settings The technology-productivity correlation: Companies often assume that the use of more advanced technology correlates directly with increased productivity. Confounding variables: The real drivers may be employee training, company culture, or other factors that affect productivity. Avoiding simplistic conclusions: Businesses must control for external factors before attributing productivity gains to technology alone. 11  26.11.2024 Correlation equals causation fallacy: One of the most frequent mistakes in interpreting statistics is assuming that Statistical correlation directly implies causation. misconceptions Ignoring third variables: Often third Common variables explain the observed correlation, such as socioeconomic fallacies in factors in educational outcomes. interpreting data Small sample size problem: Small samples can produce unreliable correlations, leading to conclusions that don’t hold in larger studies. Correlation and public perception The public often mistakenly Media amplification: Media believes that correlations outlets frequently prove causation, leading to oversimplify correlational flawed conclusions in areas findings, which can mislead like health, politics, and the public. education. 12  26.11.2024 Media often uses correlations to create sensationalist headlines… The original study in 1988 suggested that people who forced themselves to smile while looking at a cartoon found the cartoon funnier. That research was taken as gospel and the idea of “smiling to make yourself happier” spread like wildfire through psychology and decision science classes around the world. But when a number of labs tried to replicate the study in 2016, it didn’t hold up. 9 labs found a similar effect but at a much lower magnitude, and 8 labs found no effect at all, which when they combined the data came out to no significant observed effect. Ethical considerations in correlational research When experimentation is not possible: In some cases, ethical or practical constraints prevent the use of controlled experiments, leaving researchers to rely on correlations. Misinterpretation risks: Correlations can be misinterpreted, leading to flawed policy or interventions, particularly in health and education. Responsibility of researchers: Researchers must clearly communicate the limitations of correlational findings and avoid overreaching conclusions. 13  26.11.2024 Causal research: Experimental true causality Establishing true causality Random assignment: A key feature of experiments, random assignment helps eliminate third variables by distributing them equally across groups. Control groups: Using control groups allows researchers to isolate the effect of the variable being tested by comparing it to a baseline. Manipulation of variables: Researchers can actively manipulate variables to observe their direct effects on other variables. Summary and key takeaways Correlation does not mean causation: Correlations only show relationships, not causality. Misinterpreting this can lead to faulty conclusions. Third variable problem: Uncontrolled third variables can create spurious correlations, masking the real cause. Importance of experimental design: Proper experimental methods, like random assignment, are necessary to establish causality. 14  12/3/2024 PSY 179- Critical thinking in Psychology Understanding Experimental Design 1 Today Understanding experimental design 2 1  12/3/2024 Introduction to experimental control The role of control in psychological experiments Importance of control: Control in experiments ensures that changes in one variable (we call it dependent variable) are due to manipulation of another variable ( we call that one independent variable), not external factors. Minimizing bias: Proper control allows for minimization of biases such as demand characteristics and experimenter effects. Controlling extraneous variables increases the consistency and generalizability of experimental results. 3 Manipulation, comparison, and control Key elements in experimentation Manipulation of variables: Involves altering the independent variable to observe effects on the dependent variable. Comparison of groups: Comparison between experimental and control groups helps establish cause and effect relationships. Control of extraneous variables: Preventing external factors from influencing the dependent variable ensures more accurate results. 4 2  12/3/2024 Random assignment in true experiments Eliminating bias: Random assignment ensures that each participant has an equal chance of being in any group, reducing selection bias. Strengthening our results: By randomizing, we increase confidence that the one variable causes observed changes in the other variable. Generalization of results: Random assignment improves the generalizability of experimental findings across different populations. 5 Control groups: Their function and necessity 6 3  12/3/2024 Logic of true experiment manipulate an IV treatment, control conditions high degree of control especially random assignment to conditions Control group & experimental group (s) Probabilistic equivalence- the only reason why the groups would differ is by chance 7 The clever Hans phenomenon A lesson in unintended influences The case of clever Hans: Clever Hans was a horse thought to perform arithmetic, but actually responded to subtle cues from his trainer. Unintended cues: The horse’s apparent intelligence highlighted the importance of controlling for unintended, subtle cues in experiments. Influence on experimental design: This case demonstrates the importance of controlling for variables the researcher may not be aware of. 8 4  12/3/2024 Prying variables apart: Special conditions Distinguishing effects in experiments Separating variables: Researchers use specific experimental conditions to isolate the effect of individual variables. Minimizing confounds: Special conditions help in minimizing confounding variables that might distort the results. Improving consistency: Careful design of experimental conditions strengthens the consistency and accuracy of findings. 9 Intuitive psychology Challenges in experimentation Overcoming intuition: Intuitive psychology can Experiments are designed lead to incorrect to challenge and test assumptions about intuitive beliefs, revealing human behavior without insights that contradict proper controls. common sense. 10 5  12/3/2024 Conclusion: The value of control in psychology Strengthens accuracy: Control eliminates confounding factors, ensuring that the experiment measures what if claims to. Support replication: Controlled conditions allow other researchers to replicate studies, reinforcing findings across contexts. Advances psychology as a science: The use of control underpins the scientific rigor in psychology, distinguishing it from pseudoscience. 11 Key takeaways Experimental control: Central to the reliability of psychological research, ensuring valid cause-effect conclusions. Random assignment: Eliminates bias by ensuring participants are evenly distributed between groups. Control groups: Provide a baseline for comparison, helping isolate the effect of the independent variable. 12 6  12/3/2024 Experiment example: The Effect of Physical Exercise on Mood A psychologist is interested in the effect of physical exercise on mood. What will we manipulate? Physical exercise What will we measure as a result? Mood 13 What about the operational definitions? Physical exercise: 5-minute session of moderate-intensity exercise such as jogging in place or doing jumping jacks Mood: Rate their mood on a scale from 1 to 10, where 1 represents a very bad mood (e.g., sad, anxious, or stressed) and 10 represents a very good mood (e.g., happy, calm, or content) 14 7  12/3/2024 Imagine we have 20 volunteers as participants of the study Random assignment: Throw dice, use computer to randomize etc. Participants will have equal chance to be assigned to either group Experimental group (10 participant) Control group (10 participant) 10 8 Same room 6 Same 4 temperature 2 Same 0 instructions Experimental Control Measure: How do rate your mood 1 Measure: How do rate your mood 1 to to 10? 10? 15 Addressing the artificiality criticism Definition of artificiality criticism: Criticism that psychological research lacks relevance to real-world scenarios due to the artificial nature of laboratory settings. Controlled settings: In controlled experiments, variables are manipulated to determine cause and effect, enabling precise conclusions. Artificial vs. real-life settings: Though not mimicking everyday life, artificial settings help isolate key factors to provide insights into human behavior. 16 8  12/3/2024 Why natural isn’t always necessary Controlled experiments: Controlled experiments isolate key variables to determine causality in human behavior. The importance of control: In real world settings, too many factors make it hard to determine which causes affect outcomes. Lab vs. real life: Lab settings offer precision, while real world scenarios introduce uncontrolled variables. 17 Random sampling vs random assignment Random sampling: Refers to how participants are selected from a population for inclusion in a study. 18 9  12/3/2024 Random sampling vs. random assignment Random assignment: Involves assigning participants randomly to different experimental conditions to ensure balanced groups. Misunderstanding the terms: Many confuse these two concepts, but each serves a different purpose in research design. 19 Theory-driven research The role of controlled experiments in advancing psychological research Purpose of theory-driven research: Controlled experiments test theories by isolating key variables and generating empirical evidence. Building knowledge: Theories provide a framework for understanding human behavior, which is refined through experimentation. Why control is essential: Controlled settings allow researchers to evaluate the validity of theories in a systematic way. 20 10  12/3/2024 Applications of psychological theory Practical applications: Psychological theories inform treatments, educational strategies, and interventions. Evidence-based practices: Controlled research ensures that applications are based on verified principles and not anecdotal evidence. Building on theory: Successful applications often lead to further refinement and testing of the underlying theory. 21 The college sophomore problem Challenges in generalizing research findings Limited generalizability: Most psychological research involves college students, leading to concerns about whether findings apply to broader populations. Criticism of narrow sampling: Reliance on college students may limit the diversity of participants, affecting research outcomes. Addressing the issue: Researchers use different strategies, such as replication and diverse sampling, to test the accuracy of findings. 22 11  12/3/2024 Conclusion Controlled environments allow psychologists to isolate variables and test theories with precision. Validated theories find practical use in fields like education, mental health, and public policy. A continuous process: Feedback from real world applications refine and improves psychological theories over time. 23 In class activity Design a experimental study Form 3-4 person groups Think of a basic experiment of a subject. What will be your research? What will you manipulate, what will you measure? Define your variables Design your experiment and control groups Fake a graph for your results according to your predictions 24 12  10.12.2024 PSY 179- Critical thinking in Psychology Understanding statistics in psychology Today Understanding statistics in psychology 1  10.12.2024 Introduction to probabilistic reasoning Understanding the Achille’s hell of human cognition What is probabilistic reasoning?: Probabilistic reasoning refers to the cognitive process of using probabilities to make decisions. It helps individuals navigate uncertain outcomes by estimating likelihoods. Role in human cognition: Though critical for understanding the world, human cognition often struggles with applying probabilistic reasoning accurately, leading to errors in judgment. Importance in psychology: Psychology emphasizes the need for statistical thinking as most psychological findings are probabilistic rather than certain. ‘Person- who’ statistics Understanding statistical misinterpretations The ‘person-who’ fallacy refers to using a single case or anecdote to disprove a well-established statistical trend. Common misunderstanding: People often use personal examples (‘I know someone who…’) to invalidate scientific conclusions that are probabilistic. Psychological implications: This fallacy reflects a deeper cognitive challenge humans face with probabilistic reasoning. 2  10.12.2024 Misinterpreting probabilistic relationships The connection between smoking and lung cancer Oh, get outta here! Look at is probabilistic, meaning it doesn't apply to every old Joe Ferguson down at individual case. the store. Three packs of However, many misunderstand this. Camels a day since he was sixteen! Eighty-one years For instance, when a nonsmoker cites smoking old and he looks great!” statistics to persuade a smoker to quit, the smoker may respond by pointing to an exception like "Joe Ferguson," an 81-year-old who has smoked heavily for years but appears healthy. The implication is that this single case disproves the overall statistical relationship, which is a common misconception. You say job opportunities are expanding in service industries and contracting in heavy industry? No way. I know a man who got a job in a steel mill just last Thursday”; “You say families are having fewer children than they did 30 years ago? You’re crazy! The young couple next door already has three and they’re both under 30”; “You say children tend to adopt the religious beliefs of their parents? Well, I know a man at work whose son converted to another religion just the other day. ” What else?.... 3  10.12.2024 Base rate neglect Ignoring general statistical information Base neglect occurs when individuals ignore general statistical information (base rates) in favor of specific anecdotal or irrelevant information. Psychological roots: Humans tend to focus on vivid, specific cases rather than statistical data, leading to poor decision-making. Real-world example: In medical diagnoses, patients may focus on rare disease symptoms rather than considering the base rate of those diseases in the population. Example Imagine that HIV occurs in 1 out of every 1,000 people. There is a test that correctly identifies individuals with HIV but has a 5% false-positive rate, meaning it incorrectly indicates HIV in 5% of those who don’t have it. If we randomly select a person and their test result is positive, what is the probability that they actually have HIV, assuming no other information about their medical history? 4  10.12.2024 Most people, including experienced doctors, often think the chance of having HIV after a positive test result is 95%. However, the correct answer is closer to 2%. This mistake happens because people tend to focus too much on the positive test result and ignore the base rate—that only 1 in 1,000 people actually have HIV. Out of 1,000 people, only one person will likely have HIV. But with a 5% false-positive rate, the test will wrongly show that about 50 of the other 999 people also have HIV. So, out of the 51 people who test positive, only one will actually have the virus, making the true probability around 2%. Failure to use sample-size information Understanding statistical errors in probabilistic reasoning Importance of sample size: Larger samples yield more reliable estimates of population trends, while smaller samples produce more variability and extreme results. Misconceptions: People often ignore the importance of sample size, leading to faulty conclusions and exaggerated causal theories. Psychological studies: Kahneman and Tversky’s research demonstrates how people consistently fail to account for sample size in probabilistic reasoning. 5  10.12.2024 A certain town is served by two hospitals. In the larger hospital, about 45 babies are born each day, and in the smaller hospital, about 15 babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage varies from day to day. Sometimes it is higher than 50 percent, sometimes lower. For a period of one year, each hospital recorded the days on which more than 60 percent of the babies born were boys. Which hospital do you think recorded more such days? a. The larger hospital b. The smaller hospital c. About the same In this problem, most people think both the smaller and larger hospitals will have about the same chance of having a high percentage of boys born on a given day. Some choose the smaller hospital, some the larger, but the correct answer is the smaller hospital, which most people miss. The key to understanding this is sample size. In a larger hospital, with more births, the percentage of boys and girls will be closer to 50% because the larger sample is more likely to reflect the overall population. On the other hand, in a smaller hospital, the smaller number of births means it’s more likely to see big swings in the percentage—like 60% or 80% boys. The smaller sample size makes these bigger deviations more likely. 6  10.12.2024 The gambler’s fallacy Misinterpretations of random events Problem A: Imagine that we are tossing a fair coin (a coin that has a 50/50 chance of coming up heads or tails) and it has just come up heads five times in a row. For the sixth toss, do you think that ______ It is more likely that tails will come up than heads? ______ It is more likely that heads will come up than tails? ______ Heads and tails are equally probable on the sixth toss? 7  10.12.2024 The truth is, the chance of getting heads or tails is always 50% on each toss, no matter what happened before. So, after five heads, the next flip is still just as likely to be heads or tails. Past flips don’t affect future outcomes in a fair coin toss. Probabilistic reasoning in everyday life Real-life applications and misconceptions Decision-making under uncertainty: People often make decisions involving probabilities in daily life, such as evaluating risks or predicting outcomes. Misinterpretations: Common biases, like overestimating rare events (e.g. Plane crashes) and underestimating common ones (e.g. car accidents), skew reasoning. Practical impact: Improving probabilistic reasoning can lead to better financial, medical, and personal decisions. 8  10.12.2024 Common errors in judging probabilities Biases that skew probabilistic reasoning Availability heuristic: People overestimate the likelihood of events based on how easily examples come to mind, such as fearing plane crashes more than car accidents. Confirmation bias: Individuals tend to seek out information that confirms their existing beliefs, ignoring evidence that contradicts it. Representativeness heuristic: This bias leads people to judge probabilities based on how closely something matches a stereotype, ignoring actual statistical probability. Statistics and probability Enhancing decision-making Foundation for sound decisions: Understanding statistics and probability allows individuals to make more informed and rational decisions. Applications in psychology: Psychologists rely on statistical methods to analyze data and interpret research findings with probabilistic reasoning. Reducing errors: Proper use of statistical reasoning reduces common cognitive biases, leading to better decision-making outcomes. 9  10.12.2024 Cognitive strategies for improving probabilistic thinking Tools for better decision-making Frequency heuristic: Using frequency-based reasoning helps individuals estimate probabilities more accurately compared to abstract statistical formats. Framing effects: Reframing a problem in terms of gains or losses changes how individuals perceive risks and probabilities. Educating on biases: Raising awareness of common probabilistic errors, like overconfidence, enhances judgment and decision-making. Impact of probabilistic reasoning on psychology Shaping research and clinical practice Research analysis: Psychological research relies heavily on probabilistic reasoning to interpret experimental data and validate findings. Clinical decision-making: In clinical settings, therapists use probabilistic reasoning to assess risks and make treatment decisions based on probability of outcomes. Future directions: Advances in probabilistic models may improve the precision of predictions in psychological research and therapy. 10  10.12.2024 Scientific consensus in psychology The role of probabilistic models Establishing consensus: Psychological theories are validated through consensus derived from repeated experiments and probabilistic reasoning. Probabilistic evidence: Most psychological findings are based on probabilistic models that estimate likelihoods rather than certainties. Challenges and improvements: Probabilistic reasoning helps address uncertainties in research, but also requires constant refinement to improve accuracy. So far… The importance of probabilistic reasoning Crucial cognitive skill: Probabilistic reasoning is a fundamental skill in understanding skill in understanding the uncertainties of human behavior and psychological research. Applications in everyday life: Beyond psychology, probabilistic reasoning improves decision-making in fields like medicine, finance, and public policy. Educational focus: Strengthening probabilistic reasoning in education can help reduce cognitive biases and improve critical thinking. 11  10.12.2024 Introduction to chance and randomness Understanding the role of randomness in psychology Integral role of chance: Randomness plays a significant role in biological and psychological processes. Chance vs determinism: Many events are probabilistic, not determinable in advance. Importance in psychology: Recognizing the impact of chance is crucial for interpreting human behavior. Illusory correlation Perceiving patterns in randomness Seeing correlations: Humans tend to perceive connections between unrelated events. Psychological basis: Illusory correlation occurs when individuals expect a relationship between two variables, leading them to see patterns even when events are random. 12  10.12.2024 Clinical vs. actuarial prediction The superiority of actuarial methods in psychology Clinical intuition: Clinicians often rely on their subjective judgment predict patient outcomes. Actuarial prediction: Statistical methods outperform clinical predictions in numerous fields, including psychology. Research evidence: Over 100 studies confirm actuarial methods’ superiority in predicting psychotherapy outcomes and more. Challenges and resistance: Clinicians often resist actuarial methods, favoring intuition despite evidence to the contrary. Personal coincidences and randomness Understanding coincidence in psychology Coincidences as random events: Most personal coincidences are simply random occurrences, despite feeling significant. Psychological tendency: Humans are predisposed to find meaning in random coincidences, a result of pattern seeking behavior. Examples in everyday life: Coincidences such as meeting an old friend in an unexpected place are often seen as more than mere chance. 13  10.12.2024 Many musicians died at It is in fact random occurence…. the age 27: Amy We know this because of a study published in the British Medical Journal, which looked Winehouse, Kurt Cobain, at 1,046 musicians who had a No. 1 album Jim Morrison, Jimi in the UK from 1956 to 2007 (Barnett, Hendrix, Janis Joplin…. 2011). The study found no evidence that famous musicians are more likely to die at age 27. The illusion of control Belief in control: People often believe they can influence random events, Overestimating especially when they are involved in the process. influence over random events Psychological bias: The illusion of control is a cognitive bias where individuals overestimate their ability to control outcomes. Examples in gambling: Gamblers frequently believe they can control the roll of dice or shuffle of cards, despite randomness. 14  10.12.2024 Just world hypothesis and probability The cognitive bias of fairness in random events Belief in a just world: People have a tendency to believe that good things happen to good people, and bad things happen to bad people. Effect on perception of random events: This bias causes people to attribute random misfortune to personal failings, reinforcing misconceptions about randomness. Contradicting the role of chance: The just world hypothesis is often in direct conflict with the role of chance in real life events. Statistical reasoning in psychology Using probability in predict behavior Role of probability: Psychologists use statistical models to predict behavior based on probability, not certainty. Managing errors: Statistical predictions allow for managing uncertainty and error in behavioral science. Example: Clinical predictions: Probability helps in areas like therapy outcomes, where predicting exact outcomes is difficult but trends can be identified. 15  10.12.2024 Behavioral variability and probability Understanding variability in human behavior Natural behavioral variation: Individual behavior varies naturally due to both deterministic and random factors. Role of chance: Many unpredictable factors influence behavior, leading to variability even under similar conditions. Importance in psychological research: Recognizing variability is crucial in experimental design and interpretation of results. Statistical predictions in psychology The role of probability in making accurate predictions Psychological predictions: Error management: Probability in practice: Psychologists use statistical Predictions always involve While exact outcomes are models to make error margins, which uncertain, probability helps probabilistic predictions probability helps manage in identifying likely trends. about behavior. effectively. 16  10.12.2024 Examples of randomness in everyday life How random events shape our experiences Unpredictable life events: Random events like accidents or chance meetings significantly impact life trajectories. Misinterpreting randomness: Humans often try to find meaning or patterns in random occurrences, leading to false interpretations. Common examples: Lottery wins, unexpected career opportunities, and personal relationships are often shaped by random events. Conclusion: Embracing Chance as a factor: Understanding randomness in randomness is key to interpreting psychology behavior and psychological outcomes. The importance of accepting chance Managing uncertainty: Psychological predictions rely on probabilities, not certainties, making it crucial to manage uncertainty. Applications in life: Embracing randomness helps individuals make better decisions and cope with unpredictable events. 17  10.12.2024 Summary: The role of chance Chance and human behavior: Randomness is an integral factor in explaining behavioral variability and outcomes. Illusory patterns: People tend to see patterns where none exist, leading to cognitive biases. Statistical tools: Psychology relies on statistical methods to make predictions and account for uncertainty. 18  17.12.2024 PSY 179- Critical thinking in Psychology Critical Thinking in Everyday Life Today Critical thinking in everyday life 1  17.12.2024 Critical thinking in everyday life Critical thinking helps us analyze situations logically, avoid fallacies, and make well-reasoned decisions. It enhances our ability to solve problems creatively and adapt to challenges effectively. It helps in critically evaluating news sources, media content, and understanding underlying biases. Why we need to think critically in the information age Overwhelming amount of data: We are constantly exposed to a flood of data. Knowing how to discern reliable information is crucial. Rise of misinformation: Inaccurate information spreads faster than ever, making critical thinking more important. Need for independent judgment: Developing independent, rational thinking helps navigate complex issues without relying solely on unreliable sources. 2  17.12.2024 How instant information leads Speed of information sharing: to misinformation Social media and news cycles move faster than fast-checking can Understanding the dynamics keep up with. of information flow Confirmation bias: People tend to share information that supports their beliefs, regardless of its accuracy. Echo chambers: Algorithm-driven platforms create environments where misinformation is reinforced by like-minded individuals. Group dynamics and critical thinking Recognize groupthink: Understand how group pressure can lead to conformity, often suppressing critical and independent thinking. Encourage diverse perspectives: Embrace diverse opinions to challenge assumptions and improve the quality of group decisions. Practice independent analysis: Reflect on your own beliefs and question whether they are influenced by the group, or genuinely yours. 3  17.12.2024 Understanding research in media Evaluating media claims and statistics Identify misleading information: Recognize selection bias, correlation vs causation errors, and misleading visual representations in the media. Assess credibility of sources: Evaluate the reliability of news outlets, authors, and any cited research using critical questions. Understand statistical manipulations: Be attentive of data manipulation through skewed graphs or statistical misrepresentation to sway opinions. Media literacy: Navigating fake news and real news Analyze the source: Check whether the information source is trustworthy by evaluating its reputation and editorial standards. Look for emotional manipulation: Fake news often aims to evoke strong emotions. Be cautious of headlines designed to provoke fear, anger, or excitement. Cross-check information: Verify the information by checking multiple reputable sources to confirm its accuracy. 4  17.12.2024 Evaluating numbers What is plausibility? Determining the realistic nature of information Initial sanity check: Assess whether a given claim is even possible based on basic understanding and common sense. Data sources: Evaluate the credibility and reliability of the sources behind the claim. Comparative analysis: Compare data against similar know values to verify if the claim holds plausibility. 5  17.12.2024 What would you do with this claim? Is it plausible? In the thirty-five years since marijuana laws stopped being enforced in California, the number of marijuana smokers has doubled every year. Let’s assume there was only one marijuana smoker in California 35 years ago, (It’s a very conservative estimate, there were half a million marijuana arrests nationwide in 1982). and double that number every year for 35 years It would yield more than 17 billion- larger than the population of the entire world!! Try it yourself and you’ll see that doubling every year for twenty-one years gets you to over a million: 1; 2; 4; 8; 16; 32; 64; 128; 256; 512; 1024; 2048; 4096; 8192; 16,384; 32,768; 65,536; 131,072; 262,144; 524,288; 1,048,576 What would you do with this claim? Is it plausible? Our best salesperson made 1,000 sales a day. How fast can you dial a phone number yourself? Probably 5 seconds? What if you allow another 5 seconds for the phone to ring? Now, what happens if we assume that every call ends in a sale—is this a realistic scenario, or are we giving every advantage just to see if the claim works? Estimate at least 10 seconds for making a successful pitch, followed by another 40 seconds to collect the buyer's credit card details and address. What’s the duration of one call? 5+5+10+40= 60 seconds!! So you can make only 60 sales in an hour, or 480 sales in a very busy 8 hour shift with no pause!! 6  17.12.2024 Let’s evaluate this pie chart First rule of pie charts: The percentages have to add up to 100. Apparently, voters could indicate support for multiple candidates. However, the results should not be displayed using a pie chart. Types of Mean: The arithmetic average, calculated by averages: Mean, adding all values and dividing by the number of values. median and mode Median: The middle values in a set of numbers, providing a better measure when data is Understanding skewed. how averages Mode: The value that appears most frequently in a dataset, often used in categorical data. (You can be will learn more about median and mode next year at the Statistics course!) misleading 7  17.12.2024 Outliers skewing the mean: Extreme Misuse of values can significantly affect the mean, averages to making it less representative of the mislead entire dataset. How averages can be Other misleading tactics involves selectively using either median or mean, manipulated to or using mean in bimodal distributions. distort reailty You will learn more about those statistical terms next year. Example: If 10 people in a room have a total net worth of $1 million, the mean is $1 million divided by 10, which equals $100,000. The mean can sometimes be misleading, especially with outliers. Outliers are values that are significantly higher or lower than the rest of the data. If one person in a group of nine has negative wealth while the rest have about $100,000 each, the mean is $33,222. This does not accurately represent the wealth of most people in the group. 8  17.12.2024 Example: Imagine you and two friends have started a small company with five employees. As the year comes to a close, you need to present the financial report to your team, hoping to make them convinced to hire new employees. Reporting profits per employee may make the company look more efficient to justify hiring. Average salary of employees: $66,000 Average salary of owners: $100,000 Annual profits per employee: $42,000 Graphical manipulations 9  17.12.2024 Truncating axes: Truncating the y-axis can Graphical exaggerate differences in data, making manipulations: trends appear more dramatic. Scaling and Scaling manipulations: Inconsistent scales Truncating on graphs can distort perceptions of relationships between data points. How visual data representations Double Y-axis: Using two different y-axes can mislead can make unrelated variables appear correlated, misleading viewers. What can you say about this graph? Unlabeled axis: What are HCs? We aren’t told, but from the context—they’re being compared with SZ 10  17.12.2024 Unlabeled axis: The y-axis is completely missing, so we don’t know what is being measured (is it units sold or dollars?), and we don’t know what each horizontal line represents. Truncating axes: The graph visually suggests that taxes will rise significantly, with the right-hand bar being six times taller than the left-hand bar. This gives the impression of taxes increasing sixfold—something nobody would want. 11  17.12.2024 Imagine a city where crime has been growing at a rate of 5 percent per year for the last ten years. Discontinuity in Vertical or Horizontal Axis: Using all the same data, just create a discontinuity in your x-axis. This will distort the truth and deceive the eye Double Y-axis: Using two different y-axes can make unrelated variables appear correlated, misleading viewers. 12  17.12.2024 Misleading correlations: Using different scale on two y-axes can falsely imply a Double Y-axis correlation between unrelated variables. deception Visual overload: Viewers often assume How multiple Y- relationships when two data sets are shown together, even if unrelated. axes mislead viewers Manipulating perception: Adjusting scales on the y-axes can make changes seem more dramatic or aligned. Which one is true? Misleading correlations: We should look the correlations! The first graph appears to be illustrating a correlation of 0, the second graph appears to be representing one that is close to 1. The actual correlation for this dataset is.91, a very strong correlation. Spending more on students is, at least in this dataset, associated with better SAT scores. 13  17.12.2024 Visual overload: Viewers often assume relationships when two data sets are shown together, even if unrelated. Plotting Things That Are Unrelated 14  17.12.2024 The scissors are cutting the bill not at 4.2 percent of its size, but at about 42 percent. Inconsistent units: Using different units for similar data points can confuse and Inconsistent mislead viewers. data representation Changing time intervals: Altering the Distorting intervals on the x-axis can change the perceived trend of data. truths through variable manipulation Selective data exclusion: Leaving out data that doesn’t fit the narrative can make results appear more favorable. 15  17.12.2024 News reports indicated that 2014 was one of the deadliest years for plane crashes, with 22 accidents leading to 992 fatalities. However, air travel is actually safer today than ever before. Due to the vast increase in the number of flights, these 992 fatalities reflect a significant decrease in the number of deaths per million passengers (or per million miles flown). On any given flight with a major airline, the odds of dying are approximately 1 in 5 million, making it far more likely that you could be killed doing almost anything else—like crossing the street or even eating (with death from choking or accidental poisoning being roughly 1,000 times more likely). U.S. News & World Report compared the proportions of Democrats and Republicans from the 1930s onwards, but the sampling methods have changed significantly over time, making the comparisons unreliable. Early sampling favored wealthier individuals with landlines (who leaned Republican), while later methods included cell phones, which attracted younger, more Democratic-leaning respondents. Because of these evolving sampling techniques, it’s unclear if the actual proportion of Democrats to Republicans has changed, as the data over time are not truly comparable. 16  17.12.2024 Data Sampling techniques: The method of selecting participants can affect the collection reliability of data. Random sampling is ideal methods for minimizing bias. Surveys and questionnaires: The way The questions are phrased can influence responses, leading to biased data. foundation of reliable Observation and experimentation: Controlled experiments provide reliable data informatio but may lack real-world applicability. n Every time we read a result in the newspaper that 71 percent of the British are in favor of something, we should reflexively ask, “Yes, but 71 percent of which British?” 17  17.12.2024 Evaluating words How do we know? Evaluating credibility of information sources Authority of the source: Evaluate whether the source has recognized expertise or credentials in the subject area. Bias and objectivity: Assess if the source has a potential conflict of interest or inherent bias that could influence the information. Cross-referencing: Verify the information by checking multiple reputable sources to ensure accuracy. 18  17.12.2024 Credentials and experience: Check if the Identifying author has relevant education or extensive experience in the subject matter. expertise How to Peer recognition: Experts are often cited by other authorities or participate in peer- determine reviewed publications. if a source Affiliations and publications: Review the is truly an institutions or organizations the author is associated with to determine their expert reliability. Confirmation bias: We tend to favor Overlooked information that supports our existing beliefs, leading us to ignore alternative alternative explanations. explanations Complexity of causes: Most events have The multiple potential causes, and considering importance of only one can lead to oversimplification. considering Encouraging a broader view: Questioning multiple the first explanation that comes to mind possibilities helps uncover less obvious, but possibly true, alternatives. 19  17.12.2024 Hypothesis formation: The scientific method starts with a question or hypothesis that can be tested. How science Works Experimentation and observation: Conducting controlled experiments to gather The scientific data, which can either support or refute the method as a hypothesis. tool for truth Peer review and replicability: Findings must be reviewed by peers and reproduced to be accepted as valid knowledge. Knowing what you don’t know The importance of acknowledging knowledge gaps Overestimating our knowledge can lead to poor decision making, true experts recognize the limits of their understanding. Humility in learning: Acknowledging gaps in knowledge is the first step toward gaining deeper understanding and expertise. Continuous inquiry: Curiosity and asking questions are crucial to expanding our understanding and avoiding false confidence. 20  17.12.2024 Conclusion: Developing your critical thinking Understand different types of data: Learn to evaluate numbers, words, and visual information critically to identify potential manipulations. Question sources and context: Always consider the credibility of information sources and the context in which data is presented. Practice lifelong inquiry: Adopt a mindset of continuous questioning and learning to refine your understanding and resist biases. 21