Search results “Data mining bias definition psychology”
Confirmation Bias in 5 Minutes
Confirmation Bias is part of human nature. We all are susceptible to it. But why is it such a universal human condition? And what can we do to move beyond it? ----------------------------------------------------------------------------------------------------------- Subscribe To My Channel: https://www.youtube.com/channel/UCoFWz1e3VXKOoJ-E5cep1Eg Facebook: https://www.facebook.com/Thought.Monkey.Community/ Instagram: https://www.instagram.com/thought.monkey/ ----------------------------------------------------------------------------------------------------------- Music Credits: Intro - Phoniks - Got to My Head Main Video - Joakim Karud - Love Mode ----------------------------------------------------------------------------------------------------------- Sources: 1. Rider and Elephant Metaphor: The Righteous Mind by Jonathon Haidt 2. Everyone is affected by Confirmation Bias: https://www.newscientist.com/article/2129319-liberals-are-no-strangers-to-confirmation-bias-after-all/ 3. How our mind takes shortcuts: https://www.youtube.com/watch?v=Bq_xYSOZrgU 4. Kathleen White on how C02 is good for the environment: https://www.youtube.com/watch?v=xykBJLfxDFI 5. Kathleen White's credentials: https://www.desmogblog.com/kathleen-hartnett-white
Views: 49870 Thought Monkey
Examples of bias - Intro to Psychology
This video is part of an online course, Intro to Psychology. Check out the course here: https://www.udacity.com/course/ps001.
Views: 237 Udacity
What's the difference between accuracy and precision? - Matt Anticole
View full lesson: http://ed.ted.com/lessons/what-s-the-difference-between-accuracy-and-precision-matt-anticole When we measure things, most people are only worried about how accurate, or how close to the actual value, they are. Looking at the process of measurement more carefully, you will see that there is another important consideration: precision. Matt Anticole explains what exactly precision is and how can help us to measure things better. Lesson by Matt Anticole, animation by Anton Bogaty.
Views: 2804198 TED-Ed
Interpretation Bias
Even when your business metrics are accurate, you can still make bad decisions if you interpret the data incorrectly. In this video we'll review interpretation bias and how you can avoid reading things into the data that are not really there.
Views: 373 Sean Byrnes
Types of Sampling Methods (4.1)
Get access to practice questions, written summaries, and homework help on our website! http://wwww.simplelearningpro.com Follow us on Instagram http://www.instagram.com/simplelearningpro Like us on Facebook http://www.facebook.com/simplelearningpro Follow us on Twitter http://www.twitter.com/simplelearningp If you found this video helpful, please subscribe, share it with your friends and give this video a thumbs up!
Views: 297126 Simple Learning Pro
Cross Validation
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-312357973/m-438108645 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 90814 Udacity
StatQuest: MDS and PCoA
MDS (multi-dimensional scaling) and PCoA (principal coordinate analysis) are very, very similar to PCA (principal component analysis). There really only one small difference, but that difference means you need to know what you're doing if you're going to use MDS effectively. This video make sure you learn what you need to know to use MDS and PCoA. For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Stratified Sampling
An example of Stratified Sampling.
Views: 439021 Steve Mays
Identify Bias | Statistics and Probability | Chegg Tutors
Identifying sources of bias in data collection is a very important early step in designing experiments because bias can render the collected data useless. Bias in data collection is any systematic error that results in the collected data differing from the true population data. Some common examples of bias are selection bias (non-random sampling of data), measurement error (inaccurate sampling of data), undercoverage (missing parts of the population), overcoverage (including data from outside the population), and processing errors (errors made in data entry). While there are advanced statistical techniques to control for bias when unavoidable, statisticians strive to start with unbiased data. ---------- Statistics and Probability tutoring on Chegg Tutors Learn about Statistics and Probability terms like Identify Bias on Chegg Tutors. Work with live, online Statistics and Probability tutors like Fallon S. who can help you at any moment, whether at 2pm or 2am. Liked the video tutorial? Schedule lessons on-demand or schedule weekly tutoring in advance with tutors like Fallon S. Visit https://www.chegg.com/tutors/Statistics-online-tutoring/?utm_source=youtube&utm_medium=video&utm_content=managed&utm_campaign=videotutorials ---------- About Fallon S., Statistics and Probability tutor on Chegg Tutors: Indiana University of Pennsylvania, Class of 2017 Marketing, Sociology major Subjects tutored: Basic Science, Music (General), Communications, Pre-Algebra, Gender Studies, Social History, Writing, Basic Math, GED, Study Skills, AP, Economics, Political Science, Art (General), College Admissions, Ethnic Studies, Sociology, Communication, Entrepreneurship, Literature, Psychology, Resume Writing, Government, World History, English, US History, Anthropology, SAT, Marketing, European History, Algebra, Microsoft Suite, Spanish, and Environmental Science TEACHING EXPERIENCE I have been a private tutor in a variety of subjects, from Geometry to Writing to SAT Prep, for about 6 years. I have experience teaching students of all learning styles and greatly enjoy helping students reach their full potential. EXTRACURRICULAR INTERESTS I'm from a small town in Pennsylvania. Outside of school, my two biggest interests are photography and horseback riding. I just recently transferred universities and am enjoying the transition so far. I'm double majoring in Marketing and Sociology and there isn't a subject I dislike (except maybe calculus!) so I love answering all kinds of questions. Want to book a private lesson with Fallon S.? Message Fallon S. at https://www.chegg.com/tutors/online-tutors/Fallon-S-987339/?utm_source=youtube&utm_medium=video&utm_content=managed&utm_campaign=videotutorials ---------- Like what you see? Subscribe to Chegg's Youtube Channel: http://bit.ly/1PwMn3k ---------- Visit Chegg.com for purchasing or renting textbooks, getting homework help, finding an online tutor, applying for scholarships and internships, discovering colleges, and more! https://chegg.com ---------- Want more from Chegg? Follow Chegg on social media: http://instagram.com/chegg http://facebook.com/chegg http://twitter.com/chegg
Views: 4552 Chegg
Sampling Techniques [Hindi]
The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. The key benefit of probability sampling methods is that they guarantee that the sample chosen is representative of the population
Views: 136337 Manager Sahab
What do Psychology and Digital Analytics have in common? More than you might think.
Jon Boone, Digital Analyst at Cognetik, talks about the intersection between Psychology and Digital Analytics in his new video series. Read the full transcript here: http://blog.cognetik.com/2017/08/01/what-do-psychology-and-web-analytics-have-in-common-more-than-you-might-think/
Views: 193 Cognetik
Data Mining Marketing Research ChannelAide
http://www.channelaide.com/ marketing research done for your online selling
Views: 197 Mike Gerts
What is SURVIVORSHIP BIAS? What does SURVIVORSHIP BIAS mean? SURVIVORSHIP BIAS meaning & explanation
What is SURVIVORSHIP BIAS? What does SURVIVORSHIP BIAS mean? SURVIVORSHIP BIAS meaning - SURVIVORSHIP BIAS definition - SURVIVORSHIP BIAS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Survivorship bias, or survival bias, is the logical error of concentrating on the people or things that "survived" some process and inadvertently overlooking those that did not because of their lack of visibility. This can lead to false conclusions in several different ways. The survivors may be actual people, as in a medical study, or could be companies or research subjects or applicants for a job, or anything that must make it past some selection process to be considered further. Survivorship bias can lead to overly optimistic beliefs because failures are ignored, such as when companies that no longer exist are excluded from analyses of financial performance. It can also lead to the false belief that the successes in a group have some special property, rather than just coincidence (Correlation proves Causation). For example, if three of the five students with the best college grades went to the same high school, that can lead one to believe that the high school must offer an excellent education. This could be true, but the question cannot be answered without looking at the grades of all the other students from that high school, not just the ones who "survived" the top-five selection process. Survivorship bias is a type of selection bias. In finance, survivorship bias is the tendency for failed companies to be excluded from performance studies because they no longer exist. It often causes the results of studies to skew higher because only companies which were successful enough to survive until the end of the period are included. For example, a mutual fund company's selection of funds today will include only those that are successful now. Many losing funds are closed and merged into other funds to hide poor performance. In theory, 90% of extant funds could truthfully claim to have performance in the first quartile of their peers, if the peer group includes funds that have closed. In 1996, Elton, Gruber, and Blake showed that survivorship bias is larger in the small-fund sector than in large mutual funds (presumably because small funds have a high probability of folding). They estimate the size of the bias across the U.S. mutual fund industry as 0.9% per annum, where the bias is defined and measured as: "Bias is defined as average ? for surviving funds minus average ? for all funds" (Where ? is the risk-adjusted return over the S&P 500. This is the standard measure of mutual fund out-performance). Additionally, in quantitative backtesting of market performance or other characteristics, survivorship bias is the use of a current index membership set rather than using the actual constituent changes over time. Consider a backtest to 1990 to find the average performance (total return) of S&P 500 members who have paid dividends within the previous year. To use the current 500 members only and create a historical equity line of the total return of the companies that met the criteria, would be adding survivorship bias to the results. S&P maintains an index of healthy companies, removing companies that no longer meet their criteria as a representative of the large-cap U.S. stock market. Companies that had healthy growth on their way to inclusion in the S&P 500, would be counted as if they were in the index during that growth period, when they were not. Instead there may have been another company in the index that was losing market capitalization and was destined for the S&P 600 Small-cap Index, that was later removed and would not be counted in the results. Using the actual membership of the index, applying entry and exit dates to gain the appropriate return during inclusion in the index, would allow for a bias-free output.
Views: 424 The Audiopedia
Ben Fearnow - Facebook Whistleblower Reveals Trending And Bias Truths
Facebook whistleblower Ben Fearnow talks about his motivations in leaking information to the press and what impact social media has on our lives... as well as how we impact social media.
Views: 7049 The Other Stuff
ROC Curves and Area Under the Curve (AUC) Explained
An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool RESOURCES: - Transcript and screenshots: https://www.dataschool.io/roc-curves-and-auc-explained/ - Visualization: http://www.navan.name/roc/ - Research paper: http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 298643 Data School
Sampling: Simple Random, Convenience, systematic, cluster, stratified - Statistics Help
This video describes five common methods of sampling in data collection. Each has a helpful diagrammatic representation. You might like to read my blog: https://creativemaths.net/blog/
Views: 755355 Dr Nic's Maths and Stats
Sampling & its 8 Types: Research Methodology
Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling Research Methodology Population & Sample Systematic Sampling Cluster Sampling Non Probability Sampling Convenience Sampling Purposeful Sampling Extreme, Typical, Critical, or Deviant Case: Rare Intensity: Depicts interest strongly Maximum Variation: range of nationality, profession Homogeneous: similar sampling groups Stratified Purposeful: Across subcategories Mixed: Multistage which combines different sampling Sampling Politically Important Cases Purposeful Sampling Purposeful Random: If sample is larger than what can be handled & help to reduce sample size Opportunistic Sampling: Take advantage of new opportunity Confirming (support) and Disconfirming (against) Cases Theory Based or Operational Construct: interaction b/w human & environment Criterion: All above 6 feet tall Purposive: subset of large population – high level business Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow Advantages of Sampling Increases validity of research Ability to generalize results to larger population Cuts the cost of data collection Allows speedy work with less effort Better organization Greater brevity Allows comprehensive and accurate data collection Reduces non sampling error. Sampling error is however added. Population & Sample @2:25 Sampling @6:30 Systematic Sampling @9:25 Cluster Sampling @ 11:22 Non Probability Sampling @13:10 Convenience Sampling @15:02 Purposeful Sampling @16:16 Advantages of Sampling @22:34 #Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm types of sampling types of sampling pdf probability sampling types of sampling in hindi random sampling cluster sampling non probability sampling systematic sampling
Views: 351305 Examrace
K-Fold Cross Validation - Intro to Machine Learning
This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 160243 Udacity
Range, variance and standard deviation as measures of dispersion | Khan Academy
Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/e/variance?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/v/variance-of-a-population?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/box-and-whisker-plots/v/range-and-mid-range?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1283605 Khan Academy
The Problems of Publication Bias and P-Hacking, and the Potential Solution of Pre-registration
Presented by Garret Christensen of the Berkeley Initiative for Transparency in the Social Sciences.
Statistics intro: Mean, median, and mode | Data and statistics | 6th grade | Khan Academy
This is a fantastic intro to the basics of statistics. Our focus here is to help you understand the core concepts of arithmetic mean, median, and mode. Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/e/calculating-the-mean?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/mean-and-median/v/mean-median-and-mode?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/histograms/v/interpreting-histograms?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.) About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy‰Ûªs 6th grade channel: https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1901727 Khan Academy
Blind spots: Challenge assumptions
Learn more at PwC.com - http://www.pwc.com/us/en/index.jhtml Our brains are wired to make assumptions, which can sometimes be off base. We think it's an honest mistake; science calls it a blind spot.
Views: 44981 PwC
Histogram Explained
This video will show you step by step on how to create a histogram from date.
Views: 242467 cylurian
Clustering Illusion
This is the tendency to erroneously see random clusters in data as significant or meaningful. This is part of the free course on cognitive biases at https://www.virversity.com/tools/oc/courses/cognitivebiases
Views: 469 Bo Bennett
What Is A Selection Bias?
Mar 17, 2009 during world war ii, american military personnel noticed that some parts of planes were hit by enemy fire more often t"What Is A Selection Bias? Watch more videos for more knowledge Selection Bias - YouTube https://www.youtube.com/watch/Bn25u4XhYq4 Selection Bias - YouTube https://www.youtube.com/watch/iSKerlu3Pr0 Selection Bias: A Real World Example - YouTube https://www.youtube.com/watch/p52Nep7CBdQ What is SELF-SELECTION BIAS? - YouTube https://www.youtube.com/watch/XJmKR8x0wKU Self-selection bias - YouTube https://www.youtube.com/watch/wFrX5wolqP8 What Is A Selection Bias? - YouTube https://www.youtube.com/watch/Om8ZSxUhnCQ Selection bias in case-control studies - YouTube https://www.youtube.com/watch/oG027wOWJHA Bias in Sample Selection - YouTube https://www.youtube.com/watch/3zc_t1MwrPw What are the different types of BIAS? - YouTube https://www.youtube.com/watch/bDDGomrRwA4 Bias in Basic Statistics - YouTube https://www.youtube.com/watch/5K1Hg-pSY1A Selection bias as viewed as a problem with ... https://www.youtube.com/watch/s691VU9oFjc Healthy Worker Bias - YouTube https://www.youtube.com/watch/0bhsDbIrW24 Misclassification bias - YouTube https://www.youtube.com/watch/pPyZgeQK24U Random assignment - removes selection bias - YouTube https://www.youtube.com/watch/pE6zYPjFh4A Threats To Internal Validity - Selection Biases - Nerd ... https://www.youtube.com/watch/d1MKMlHi-uc What is PARTICIPATION BIAS? What does ... https://www.youtube.com/watch/GP7X4cXzYX4 Contextual bias – why nothing exists in isolation - YouTube https://www.youtube.com/watch/6DSVtO03Bek Understanding publication bias (03:09 min) - YouTube https://www.youtube.com/watch/z6E4vljXrNU Understanding unconscious bias - YouTube https://www.youtube.com/watch/dVp9Z5k0dEE USMLE Step 1 Epidemiology Principles: Bias - YouTube https://www.youtube.com/watch/a6dlA8kgmQM" mpling group to be selected selection bias is a kind of that occurs when the researcher decides who going studied. Beware the dangers of selection bias harvard business reviewpsychology glossary identifying and avoiding in research ncbi nih. Edu otlt mph ep bias ep713_bias_print. This statistics glossary includes definitions of all technical terms used definition selection bias statistical error that causes a in the sampling portion an experiment. Fem selection bias and cohort studiesbias statistical analysis handbook. Selection bias definition stat trek. The bias exists due to a flaw in the sample selection process, where subset of aug 18, 2014. What is selection bias? (and how to defeat it) imotions. With observational studies such as cohort, case control and cross sectional studies) selection bias is a distortion in measure of association (such risk ratio) due to sample that does not accurately reflect the target population type caused by choosing non random data for statistical analysis. Selection bias wikipediaselection wikipedia. Selection bias sph sphweb. Googleusercontent search. Definition of selection bias nci dictionary cancer terms & survivorship. It is sometimes referred to as the selection effect definition of bias, from stat trek dictionary statistical terms and concepts. Selection bias a real world example youtube. The ideal study population is clearly defined, accessible, reliable, and at increased risk to jan 1, 2011 self selection bias the problem that very often results when survey respondents are allowed decide entirely for themselves whether or not may occur in cohort studies if exposed unexposed groups truly comparable [1], e. Selection bias the skeptic's dictionary skepdic. Comparing an occupational cohort with the selection bias is a systematic error in study that occurs from process used to identify (select) participants, allocate them groups and term bias, statistical context, has variety of meanings. They analyzed the bullet holes in returning planes and launched a program to have these areas reinforced so that they could withstand enemy psychology definition for selection bias normal everyday language, edited by psychologists, professors leading students. Html url? Q webcache. Institute for work & healthsample selection bias investopedia. Selection bias wikipedia. Ideally, the subjects in a study should be very jul 29, 2013 explanation of what is bias statisticsarticles on ap statistics and elementary statistics, videos nov 8, 2016 good research begins well before first experiment starts. Help us get better selection bias may occur during identification of the study population. How does selection bias interfere with good research, and how can we prevent it? . What is selection bias? Definition and meaning businessdictionary bias. Self selection bias sage r
Views: 133 Question Tray
Research 2.0: Confirmation Bias as a Human Aspect in Software Engineering
Background: Data mining methods are used in empirical software engineering research to predict, diagnose, and plan for various tasks during the software development process. Such prediction models enhance managerial decision making. All the techniques so far used product and process related metrics in building predictive models. Aims: Software is designed, implemented and tested by people. Therefore, it is important to gain insight about people�s thought processes and their problem solving skills in order to improve software quality. While solving problems during any phase of the Software Development Life Cycle (SDLC), software engineers employ some heuristics. These heuristics may result in �cognitive biases�, which are defined as patterned deviations of human thought from the laws of logic and mathematics. In this research, we focused on a specific cognitive bias called �confirmation bias�, which is defined as the tendency of people to seek evidence that verifies a hypothesis rather than seeking evidence to falsify a hypothesis. Method: We defined a methodology to quantify/measure confirmation biases of software engineers by inheriting theories from the grounded work in cognitive psychology literature. We have come up with a �confirmation bias metrics set�. Results: Our empirical results demonstrated that developers� confirmation biases have a significant impact on the defect proneness of software. We found that individuals who have been trained in logical reasoning and hypotheses testing techniques exhibit less confirmatory behavior. By using developers� confirmation bias metrics values as input, we built learning-based models to predict defective parts of software, in addition to building models that are learned from static code and churn metrics. The performance of defect prediction models built using only confirmation bias metrics was found to be comparable with the performance of the models that use static code and/or churn metrics. Conclusions: We believe that next generation of empirical research in software engineering will bring more value to practice through better understanding of developer characteristics. Tool support is also necessary to measure, store and analyze such characteristics.
Views: 164 Microsoft Research
Machine Learning Tutorial 4  - Generalization (Algorithms)
Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning Generalization (Algorithms) is 4th in this machine learning course. This video explains an algorithm's ability to generalize beyond data that we have available. This allows the algorithm to choose the best model even if we are lacking historical data to fully represent reality. Consider also generalization as a measurement of how well an algorithm is able to predict an entity's target feature value even though we do not have historical data to match such entity. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 4703 Caleb Curry
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 417686 CrashCourse
Base Rate Fallacy
3 mins on base rate fallacy, take a look!
Views: 2575 Chistie Yang
Scientific Studies: Last Week Tonight with John Oliver (HBO)
John Oliver discusses how and why media outlets so often report untrue or incomplete information as science. Connect with Last Week Tonight online... Subscribe to the Last Week Tonight YouTube channel for more almost news as it almost happens: www.youtube.com/user/LastWeekTonight Find Last Week Tonight on Facebook like your mom would: http://Facebook.com/LastWeekTonight Follow us on Twitter for news about jokes and jokes about news: http://Twitter.com/LastWeekTonight Visit our official site for all that other stuff at once: http://www.hbo.com/lastweektonight
Views: 14427970 LastWeekTonight
Understanding Confidence Intervals: Statistics Help
This short video gives an explanation of the concept of confidence intervals, with helpful diagrams and examples. Find out more on Statistics Learning Centre: http://statslc.com or to see more of our videos: https://wp.me/p24HeL-u6
Views: 748528 Dr Nic's Maths and Stats
What is FORENSIC PROFILING? What does FORENSIC PROFILING mean? FORENSIC PROFILING meaning - FORENSIC PROFILING definition - FORENSIC PROFILING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Forensic profiling is the study of trace evidence in order to develop information which can be used by police authorities . This information can be used to identify suspects and convict them in a court of law. The term "forensic" in this context refers to "information that is used in court as evidence" (Geradts & Sommer 2006, p. 10). The traces originate from criminal or litigious activities themselves. However traces are information that is not strictly dedicated to the court. They may increase knowledge in broader domains linked to security that deal with investigation, intelligence, surveillance, or risk analysis (Geradts & Sommer 2008, p. 26). Forensic profiling is different than offender profiling, which only refers to the identification of an offender to the psychological profile of a criminal. In particular, forensic profiling should refer to profiling in the information sciences sense, i.e., to "The process of 'discovering' correlations between data in data bases that can be used to identify and represent a human or nonhuman subject (individual or group), and/or the application of profiles (sets of correlated data) to individuate and represent a subject or to identify a subject as a member of a group or category" (Geradts & Sommer 2006, p. 41). Forensic profiling is generally conducted using datamining technology, as a means by which relevant patterns are discovered, and profiles are generated from large quantities of data. A distinction of forms of profiles that are used in a given context is necessary before evaluating applications of data mining techniques for forensic profiling. The data available to law enforcement agencies are divided into two categories (Geradts & Sommer 2008, p. 15): Nominal data directly designates persons or objects (recidivists, intelligence files and suspect files, stolen vehicles or objects, etc.) and their relations. Nominal data may also be obtained in the framework of specific investigations, for instance a list of calls made with a mobile phone (card and/or phone) that cover a certain period of time, a list of people corresponding to a certain profile, or data obtained through surveillances; Crime data consist of traces that result from criminal activities: physical traces, other information collected at the scene, from witness or victims or some electronic traces, as well as reconstructed descriptions of cases (modus operandi, time intervals, duration and place) and their relations (links between cases, series). The use of profiling techniques represents threats to the privacy of the individual and to the protection of fundamental freedoms. Indeed, criminal data, i.e., data which are collected and processed for suppressing criminal offences, often consists of personal data. One of the issues is the re-use of personal data collected within one criminal investigation for another purpose than the one for which it was collected. Several methods-including technical, legal, and behavioral-are available to address some of the issues associated with forensic profiling. For instance, in Europe the European Convention on Human Rights provides a number of instruments for the Protection of Individuals with regard to Automatic Processing of Personal Data.
Views: 406 The Audiopedia
Making friends | Ourn Sarath
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Views: 16512 Ourn Sarath
2015 How to use the McMaster data extraction tools for a Critical Review
Two examples of articles reviewed using the McMaster Critical Review data extraction tools
Sampling in Psychological research Part 1
Definition, description
Views: 196 Tariq Mahmood Malik
The p-Hack Rap: Scientific Replicability Explained Through Rhymes - Daniel Rosenfeld
Trying to publish in journals That have a high impact But quite often, in fact Methods are far from intact With a p-hack we see that We lack a priori testing But in a system where novelty Is viewed as the best thing Validity flies out the door As we test moderators and more Lose sight of reality Fearing a boot through the door Fearing being that person Who has a low H-index Because their analyses were clean With stats morals like Windex Stuck yielding null findings Not sticking to the binding Conditions that define Any hypothesis any time Maybe delete this outlier Only one, just to try it And unknowingly succumb To sheer confirmation bias Or one can start hypothesizing After results are known Just to make a novel claim That in reality is overblown Like a fisherman in the sea One can reel for false positives Uncover a novel discovery When in reality it's the opposite Searching data like it's a gold mine Trying to earn a good merit Need a p less than .05 Cause it's publish or perish But together we can revise Conventional norms and systems As a field we can rise Toward methodological wisdom We need greater transparency To beat this replication crisis To overcome biases that emerge When left to our own opaque devices Revision starts with new visions That ignite new ignitions Preregistration will reveal All those post hoc decisions
Views: 726 Daniel Rosenfeld
Signal Detection Theory: Cognitive Psychology - Dr. Boaz Ben David
Movie: Signal Detection Theory Course: Cognitive Psychology Lecturer: Dr. Boaz Ben David, Psychology school --- Advanced Learning Technologies unit, IDC Herzliya Visit us: alt.idc.ac.il
The Ocean Knows: Psychometrics in the 21st Century
News References https://www.dasmagazin.ch/ https://motherboard.vice.com/en_us/article/how-our-likes-helped-trump-win https://antidotezine.com/2017/01/22/trump-knows-you/ https://www.washingtonpost.com/politics/after-working-for-trumps-campaign-british-data-firm-eyes-new-us-government-contracts/2017/02/17/a6dee3c6-f40c-11e6-8d72-263470bf0401_story.html?utm_term=.77a17dec01ef Mercer/Cruz https://www.nytimes.com/2016/08/19/us/politics/robert-mercer-donald-trump-donor.html http://www.politico.com/story/2015/07/ted-cruz-donor-for-data-119813 https://www.bloomberg.com/news/articles/2016-10-27/inside-the-trump-bunker-with-12-days-to-go Kosinski papers http://www.pnas.org/content/110/15/5802.full http://www.pnas.org/content/112/4/1036.full https://mathbabe.org/2016/08/11/donald-trump-is-like-a-biased-machine-learning-algorithm/ https://applymagicsauce.com/demo.html https://cambridgeanalytica.org/about All copyrights belong to their respective owners. All materials used for making this video, are for criticizing and/or for education purpose, and therefore, we acknowledge in good faith that, the content here meets the legal requirements for fair use or fair dealing under applicable copyright laws.
Views: 4144 kris kelvin
HOW TO ANALYZE PEOPLE ON SIGHT - FULL AudioBook - Human Analysis, Psychology, Body Language
How To Analyze People On Sight | GreatestAudioBooks 🎅 Give the gift of audiobooks! 🎄 Click here: http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8&a_bid=ec49a209 🌟SPECIAL OFFERS: ► Free 30 day Audible Trial & Get 2 Free Audiobooks: https://amzn.to/2Iu08SE ...OR: 🌟 try Audiobooks.com 🎧for FREE! : http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8 ► Shop for books & gifts: https://www.amazon.com/shop/GreatestAudioBooks How To Analyze People On Sight | GreatestAudioBooks by Elsie Lincoln Benedict & Ralph Pain Benedict - Human Analysis, Psychology, Body Language - In this popular American book from the 1920s, "self-help" author Elsie Lincoln Benedict makes pseudo-scientific claims of Human Analysis, proposing that all humans fit into specific five sub-types. Supposedly based on evolutionary theory, it is claimed that distinctive traits can be foretold through analysis of outward appearance. While not considered to be a serious work by the scientific community, "How To Analyze People On Sight" makes for an entertaining read. . ► Follow Us On TWITTER: https://www.twitter.com/GAudioBooks ► Friend Us On FACEBOOK: http://www.Facebook.com/GreatestAudioBooks ► For FREE SPECIAL AUDIOBOOK OFFERS & MORE: http://www.GreatestAudioBooks.com ► SUBSCRIBE to Greatest Audio Books: http://www.youtube.com/GreatestAudioBooks ► BUY T-SHIRTS & MORE: http://bit.ly/1akteBP ► Visit our WEBSITE: http://www.GreatestAudioBooks.com READ along by clicking (CC) for Caption Transcript LISTEN to the entire book for free! Chapter and Chapter & START TIMES: 01 - Front matter -- - 00:00 02 - Human Analysis - 04:24 03 - Chapter 1, part 1 The Alimentive Type - 46:00 04 - Chapter 1, part 2 The Alimentive Type - 1:08:20 05 - Chapter 2, part 1 The Thoracic Type - 1:38:44 06 - Chapter 2, part 2 The Thoracic Type - 2:10:52 07 - Chapter 3, part 1 The Muscular type - 2:39:24 08 - Chapter 3, part 2 The Muscular type - 3:00:01 09 - Chapter 4, part 1 The Osseous Type - 3:22:01 10 - Chapter 4, part 2 The Osseous Type - 3:43:50 11 - Chapter 5, part 1 The Cerebral Type - 4:06:11 12 - Chapter 5, part 2 The Cerebral Type - 4:27:09 13 - Chapter 6, part 1 Types That Should and Should Not Marry Each Other - 4:53:15 14 - Chapter 6, part 2 Types That Should and Should Not Marry Each Other - 5:17:29 15 - Chapter 7, part 1 Vocations For Each Type - 5:48:43 16 - Chapter 7, part 2 Vocations For Each Type - 6:15:29 #audiobook #audiobooks #freeaudiobooks #greatestaudiobooks #book #books #free #top #best #psychology # This video: Copyright 2012. Greatest Audio Books. All Rights Reserved. Audio content is a Librivox recording. All Librivox recordings are in the public domain. For more information or to volunteer visit librivox.org. Disclaimer: As an Amazon Associate we earn from qualifying purchases. Your purchases through Amazon affiliate links generate revenue for this channel. Thank you for your support.
Views: 2082428 Greatest AudioBooks
Social Network Analysis
An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 4608 Microsoft Research
Antisocial Computing: Explaining and Predicting Negative Behavior Online (Justin Cheng)
Antisocial behavior and misinformation are increasingly prevalent online. As users interact with one another on social platforms, negative interactions can cascade, resulting in complex changes in behavior that are difficult to predict. My research introduces computational methods for explaining the causes of such negative behavior and for predicting its spread in online communities. It complements data mining with crowdsourcing, which enables both large-scale analysis that is ecologically valid and experiments that establish causality. First, in contrast to past literature which has characterized trolling as confined to a vocal, antisocial minority, I instead demonstrate that ordinary individuals, under the right circumstances, can become trolls, and that this behavior can percolate and escalate through a community. Second, despite prior work arguing that such behavioral and informational cascades are fundamentally unpredictable, I demonstrate how their future growth can be reliably predicted. Through revealing the mechanisms of antisocial behavior online, my work explores a future where systems can better mediate interpersonal interactions and instead promote the spread of positive norms in communities. . . . . . . . . . . . . . . Justin Cheng is a Ph.D. candidate in computer science at Stanford University, where he is advised by Jure Leskovec and Michael Bernstein. His research is at the intersection of data science and human-computer interaction, and focuses on cascading behavior in social networks. This work has received a best paper award, as well as several best paper nominations at CHI, CSCW, ICWSM, and WWW. He is also a recipient of a Microsoft Research Ph.D. fellowship and a Stanford Graduate Fellowship. More information: https://www.ischool.berkeley.edu/events/2017/antisocial-computing-explaining-and-predicting-negative-behavior-online
Life After P-hacking
Joseph Simmons, Associate Editor of Management Science -- Judgement and Decision Making, discusses p-hacking and effective changes to improve the way researchers do science. This presentation is recorded as part of the University of Florida Warrington College of Business' Reliable Research in Business initiative. To watch more videos about reliable research practices, please sign up here: https://warrington.ufl.edu/reliable-research-in-business/best-practices-for-reliable-research/.
Views: 359 UFWarrington
Neuroskeptic: P-Hacking: A How-To Guide
A talk on "P-Hacking" by the pseudo-anonymous blogger Neuroskeptic. Summary: "P-Hacking" is a popular tool for extracting positive results from negative data. It's so easy, you can even do it unconsciously. But how does p-hacking work and why is it so popular? In this talk, Neuroskeptic discusses some of the top ways of hacking data, and shows the power of p-hacking by means of a live demonstration. Neuroskeptic also discusses how p-hacking can be detected and prevented.
Views: 8677 TARG Bristol
What is BIODATA? What does BIODATA mean? BIODATA meaning, definition & explanation
✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is BIODATA? What does BIODATA mean? BIODATA meaning, definition & explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In industrial and organizational psychology, biodata is biographical data. is “...factual kinds of questions about life and work experiences, as well as items involving opinions, values, beliefs, and attitudes that reflect a historical perspective.” Since the respondent replies to questions about themselves, there are elements of both biography and autobiography. The basis of biodata’s predictive abilities is the axiom that past behaviour is the best predictor of future behaviour. Biographical information is not expected to predict all future behaviours but it is useful in personnel selection in that it can give an indication of probable future behaviours based on an individual’s prior learning history. Biodata instruments (also called Biographical Information Blanks) have an advantage over personality and interest inventories in that they can capture directly the past behaviour of a person, probably the best predictor of his or her future actions. These measures deal with facts about the person’s life, not introspections and subjective judgements. Over the years, personnel selection has relied on standardized psychological tests. The five major categories for these tests are intellectual abilities, spatial and mechanical abilities, perceptual accuracy, motor abilities and personality tests. The mean coefficient for a standardized test of g is 0.51. A review of 58 studies on biodata found coefficients that ranged from 0.32 to 0.46 with a mean validity of 0.35. The mean validity of interviews was found to be 0.19. research has indicated a validity coefficient of 0.29 for unstructured interviews and 0.31 for structured interviews but interview results can be affected by interviewer biases and have been challenged in a number of different court cases. In summary, Biodata has been shown to be a valid and reliable means to predict future performance based on an applicant’s past performance. A well-constructed Biodata instruments is legally defendable and unlike the interview, is not susceptible to error due to rater biases or the halo effect. It has proven its worth in personnel selection as a cost effective tool. In the South Asian community (Nepal, India, Pakistan, Bangladesh), a biodata is essentially a résumé. The purpose is similar to that of a résumé, to eliminate some candidates from the pool of prospective suitors before meeting others. The biodata generally contains the same type of information as a résumé (i.e. objective, work history, salary information, educational background), but may also include physical attributes, such as height, weight, hair/skin/eye color, and a photo.
Views: 13898 The Audiopedia
Why creating AI that has free will would be a huge mistake | Joanna Bryson
AI expert Joanna Bryson posits that giving artificial intelligence the same rights a human has could result in pretty dire consequences... because AI has already proven that it can pick up negative human characteristics if those characteristics are in the data. Therefore, it's not crazy at all to think that AI could scan every YouTube comment in one afternoon and pick up all the negativity we've unloaded there. If it's already proven it's not only capable of making the wrong decision but eventually will make the wrong decision when it comes to data mining and implementation, why even give it the same powers as us in the first place? Read more at BigThink.com: http://bigthink.com/videos/joanna-bryson-why-creating-an-ai-that-has-free-will-would-be-a-huge-mistake Follow Big Think here: YouTube: http://goo.gl/CPTsV5 Facebook: https://www.facebook.com/BigThinkdotcom Twitter: https://twitter.com/bigthink Joanna Bryson: First of all there’s the whole question about why is it that we in the first place assume that we have obligations towards robots? So we think that if something is intelligent, then that’s their special source, that’s why we have moral obligations. And why do we think that? Because most of our moral obligations, the most important thing to us is each other. So basically morality and ethics are the way that we maintain human society, including by doing things like keeping the environment okay, you know, making it so we can live. So, one of the way we characterize ourselves is as intelligent, and so when we then see something else and say, “Oh it’s more intelligent, well then maybe it needs even more protection.” In AI we call that kind of reasoning heuristic reasoning: it’s a good guess that will probably get you pretty far, but it isn’t necessarily true. I mean, again, how you define the term “intelligent” will vary. If you mean by “intelligent” a moral agent, you know, something that’s responsible for its actions, well then, of course, intelligence implies moral agency. When will we know for sure that we need to worry about robots? Well, there’s a lot of questions there, but consciousness is another one of those words. The word I like to use is “moral patient”. It’s a technical term that the philosophers came up with, and it means, exactly, something that we are obliged to take care of. So now we can have this conversation. If you just mean “conscious means moral patient”, then it’s no great assumption to say “well then, if it’s conscious then we need to take care of it”. But it’s way more cool if you can say, “Does consciousness necessitate moral patiency?” And then we can sit down and say, “well, it depends what you mean by consciousness.” People use consciousness to mean a lot of different things. So one of the things that we did last year, which was pretty cool, the headlines, because we were replicating some psychology stuff about implicit bias—actually the best one is something like “Scientists Show That A.I. Is Sexist and Racist, and It’s Our Fault,” which that’s pretty accurate, because it really is about picking things up from our society. Anyway, the point was, so here is an AI system that is so human-like that it’s picked up our prejudices and whatever… and it’s just vectors! It’s not an ape. It’s not going to take over the world. It’s not going to do anything, it’s just a representation; it’s like a photograph. We can’t trust our intuitions about these things. We give things rights because that’s the best way we can find to handle very complicated situations. And the things that we give rights are basically people. I mean some people argue about animals, but technically, and again this depends on whose technical definition you use, but technically rights are usually things that come with responsibilities and that you can defend in a court of law.
Views: 18233 Big Think
Three Principles of Data Science: Predictability, Stability, and Computability
In this talk, I will discuss intertwining importance and connections of three principles of data science. The three principles will be demonstrated in the context of two neuroscience projects and through analytical connections. In particular, the first project adds stability to predictive models used for reconstruction of movies from fMRI brain signals to gain interpretability of the predictive models. The second project employs predictive transfer learning and stable (manifold) deep dream images to characterize the difficult V4 neurons in primate vision cortex. Our results lend support, to a certain extent, to the resemblance to a primate brain of Convolutional Neural Networks (CNNs).  See more on this video from https://www.microsoft.com/en-us/research/video/three-principles-of-data-science-predictability-stability-and-computability/
Views: 851 Microsoft Research
Tutorial: 21 fairness definitions and their politics
Computer scientists and statisticians have devised numerous mathematical criteria to define what it means for a classifier or a model to be fair. The proliferation of these definitions represents an attempt to make technical sense of the complex, shifting social understanding of fairness. Thus, these definitions are laden with values and politics, and seemingly technical discussions about mathematical definitions in fact implicate weighty normative questions. A core component of these technical discussions has been the discovery of trade-offs between different (mathematical) notions of fairness; these trade-offs deserve attention beyond the technical community. This tutorial has two goals. The first is to explain the technical definitions. In doing so, I will aim to make explicit the values embedded in each of them. This will help policymakers and others better understand what is truly at stake in debates about fairness criteria (such as individual fairness versus group fairness, or statistical parity versus error-rate equality). It will also help computer scientists recognize that the proliferation of definitions is to be celebrated, not shunned, and that the search for one true definition is not a fruitful direction, as technical considerations cannot adjudicate moral debates. My second goal is to highlight technical observations and discoveries that deserve broader consideration. Many of these can be seen as “trolley problems” for algorithmic fairness​, and beg to be connected to philosophical theories of justice. I hope to make it easier for ethics scholars, philosophers, and domain experts to approach this territory. === A couple of slides in this video have been edited with additional text compared to the version presented at FAT*.
Views: 7024 Arvind Narayanan
Mathematical Models of Decision Making Processes: State of the Art and Challenges. UCM
VII Advanced International Seminar Mathematical Models of Decision Making Processes: State of the Art and Challenges Salón de Grados Facultad de Psicología Universidad Complutense de Madrid Organized by Rocío Alcalá-Quintana and Berenice López-Casal Every action comes out of a choice. The cognitive mechanisms that govern these choices and the social, political, and economic patterns that emerge from them have been a subject of interest for behavioral scientists across disciplines. In this context, Psychology is concerned with explaining how decisions are made based on evidence, personal preferences, and individual criteria. Thus, formalizing human decision strategies, including a variety of biases and heuristics, is pivotal in understanding almost any human behavior. Because of their ubiquity, decision-making processes hold particular relevance in experimental psychology, but they are often overlooked. Observers’ responses to experimental tasks are customarily taken as direct measures of the cognitive phenomenon of interest, even though decision processes unavoidably mediate those responses. Disregarding the decisional biases and judgment errors that take place during data collection has been shown to obscure theoretically relevant patterns and to contaminate them via methodological artifacts. Our success in developing meaningful theories about any aspect of human cognition is, thus, heavily dependent on our ability to devise dependable explanations of choice mechanisms. Mathematical modelling has proven to be helpful in disentangling the effects of the various psychological processes that mediate observed performance. This seminar brings together seven renowned specialists who can offer a state-of-the-art view of quantitative decision-making models across different psychological domains, with a focus on three prominent frameworks within Mathematical Psychology: axiomatic choice models, perceptual decision-making models, and quantum cognition. Presentations will be followed by a discussion aimed at highlighting equivalences, connections, and distinctions among the different frameworks, also acknowledging the different challenges they face. We hope to provide researchers with tools to address the study of decision processes, as well as present them with promising modeling paradigms that receive little attention in undergraduate and graduate Psychology programs.
Facebook CEO Mark Zuckerberg testifies before Congress on data scandal
Facebook CEO Mark Zuckerberg will testify today before a U.S. congressional hearing about the use of Facebook data to target voters in the 2016 election. Zuckerberg is expected to offer a public apology after revelations that Cambridge Analytica, a data-mining firm affiliated with Donald Trump's presidential campaign, gathered personal information about 87 million users to try to influence elections. »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 132572 CBC News