Short talk on 2 cognitive biases and reproducibility
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Cognitive biases like schemata and confirmation bias can lead researchers to see patterns in random data and selectively remember results that agree with their hypotheses. This can contribute to poor reproducibility and replicability in science. Even when trials are registered, publication and reporting biases still result in an overestimation of treatment effects. Researchers must be aware of how these biases can influence their work at every stage, from initial data collection and analysis to citation of prior studies. Proper understanding of statistical concepts like p-values is also important to avoid misinterpreting results.
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009906275.pdf
1. The document discusses several key concepts in psychology including intuition, common sense, the scientific method, experimentation, correlation, causation, statistical analysis and making inferences from data. It provides examples to illustrate limits of intuition and use of various research methods.
2. Key research methods covered include case studies, surveys, naturalistic observation, experiments and correlation research. Steps of the scientific method and experimentation are outlined.
3. The importance of statistical analysis for interpreting data is emphasized. Concepts like measures of central tendency, variation, distributions and determining statistical significance are examined.
Sti2018 jws
Paper presentation at the STI 2018 conference in Leiden, the Netherlands.
Science and Technology indicators
Research methods Ch. 2
Psychology research methods aim to test hypotheses scientifically. Researchers must account for biases that skew logic, such as hindsight bias, overconfidence, and the Barnum effect. There are two main research methods: experimental and correlational. Experimental research manipulates an independent variable to determine its causal effect on a dependent variable, while controlling for confounding variables through random assignment and double-blind procedures. Correlational research observes relationships between variables without manipulating them. Proper research requires following ethical guidelines to protect human and animal subjects.
Cognitive biases - Logic vs. The brain
This document discusses cognitive biases and how the human brain reasons compared to computers and robots. It explains that the brain assigns meaning to information in a way that can introduce logical errors, unlike computers which process data in a purely logical manner. Several common cognitive biases are described such as confirmation bias, conjunction fallacy, anchoring effect, and regression to the mean. Examples of animal cognition experiments in gorillas, dogs, and horses are provided to show how non-human animals can also exhibit logical reasoning abilities based on experience. The document concludes by offering tips on how to mitigate the impact of cognitive biases in decision making.
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Correlation
PSYCH/610 Version 2
1
University of Phoenix Material
Correlation
A researcher is interested in investigating the relationship between viewing time (in seconds) and ratings of aesthetic appreciation. Participants are asked to view a painting for as long as they like. Time (in seconds) is measured. After the viewing time, the researcher asks the participants to provide a ‘preference rating’ for the painting on a scale ranging from 1-10. Create a scatter plot depicting the following data:
Viewing Time in Seconds
Preference Rating
10
3
12
4
24
7
5
3
16
6
3
4
11
4
5
2
21
8
23
9
9
5
3
3
17
5
14
6
What does the scatter plot suggest about the relationship between viewing time and aesthetic preference? Is it accurate to state that longer viewing times are the result of greater preference for paintings? Explain. Submit your scatter plot and your answers to the questions to your instructor.
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES.In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements ab.
Chapter 1 Ap Psych- Research Methods
The document introduces several key concepts in psychology including intuition, common sense, psychological science, critical thinking, the scientific method, theories, hypotheses, research methods, experiments, and statistical analysis. It discusses how psychology aims to provide a scientific understanding of human behavior and mental processes through rigorous empirical study rather than relying solely on intuition or common sense.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docx
Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.
Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred th ...
Module 2 research strategies how psychologists ask and answer questions
The document discusses various aspects of the scientific method used by psychologists to study behavior and mental processes. It explains that intuition and common sense can be inaccurate, while a scientific approach using steps like asking questions, constructing hypotheses, experimentation, analysis and communication of results can provide more reliable insights. Some examples discussed include how hindsight bias and overconfidence can affect perceptions, and the importance of objectively observing and describing behavior systematically through methods like case studies, surveys and naturalistic observation.
Statistics in clinical and translational research common pitfalls
Lecture on clinical decision making to the Jacobi Medical Center / AECOM Internal Medicine Residents using examples from the COVID-19 literature.
3. hypothesis
This document defines hypothesis and discusses its functions and characteristics. It begins by defining a hypothesis as an intelligent guess or prediction that gives direction to a researcher to answer a research question. It then explains that a hypothesis formally states the expected relationship between two or more variables. The document outlines several key contributions and functions of hypotheses, including providing clarity to research problems and objectives, directing the research study process, and facilitating data collection and analysis. It describes different types of hypotheses, such as null/alternative hypotheses, directional/non-directional, simple/complex, and causal/associative hypotheses. Finally, it discusses advantages and disadvantages of stating hypotheses and emphasizes the importance of hypotheses in providing relationships between variables and objectivity to research.
Similar to Short talk on 2 cognitive biases and reproducibility (20)
Myers 9e ch1 - Thinking Critically with Psychological Science
Myers 9e ch1 - Thinking Critically with Psychological Science
Topic Learning TeamNumber of Pages 2 (Double Spaced)Num.docx
Topic Learning TeamNumber of Pages 2 (Double Spaced)Num.docx
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docx
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docx
Module 2 research strategies how psychologists ask and answer questions
Module 2 research strategies how psychologists ask and answer questions
Statistics in clinical and translational research common pitfalls
Statistics in clinical and translational research common pitfalls
More from Dorothy Bishop
Exercise/fish oil intervention for dyslexia
Slides from talk at 2008 meeting of British Dyslexia Association. Critical analysis of Dore intervention approach and fish oil supplementation
Open Research Practices in the Age of a Papermill Pandemic
Talk given to Open Research Group, Maynooth University, October 2022.
Describes the phenomenon of large-scale fraudulent science publishing (papermills), and discusses how open science practices can help tackle this.
Language-impaired preschoolers: A follow-up into adolescence.
Stothard, S. E., Snowling, M. J., Bishop, D. V., Chipchase, B. B., & Kaplan, C. A. (1998). Language-impaired preschoolers: A follow-up into adolescence. Journal of Speech, Language, and Hearing Research: JSLHR, 41(2), 407–418. https://doi.org/10.1044/jslhr.4102.407
ABSTRACT: This paper reports a longitudinal follow-up of 71 adolescents with a preschool history of speech-language impairment, originally studied by Bishop and Edmundson (1987). These children had been subdivided at 4 years into those with nonverbal IQ 2 SD below the mean (General Delay group), and those with normal nonverbal intelligence (SLI group). At age 5;6 the SLI group was subdivided into those whose language problems had resolved, and those with persistent SLI. The General Delay group was also followed up. At age 15-16 years, these children were compared with age-matched normal-language controls on a battery of tests of spoken language and literacy skills. Children whose language problems had resolved did not differ from controls on tests of vocabulary and language comprehension skills. However, they performed significantly less well on tests of phonological processing and literacy skill. Children who still had significant language difficulties at 5;6 had significant impairments in all aspects of spoken and written language functioning, as did children classified as having a general delay. These children fell further and further behind their peer group in vocabulary growth over time.
Journal club summary: Open Science save lives
Slides based on preprint by Besançon et al, considering rush of COVID-19 research, much of poor quality
Otitis media with effusion: an illustration of ascertainment bias
Otitis media with effusion (OME) provides an example of how ascertainment bias can induce spurious correlations. Early work suggested it impacted children's language, but when unbiased samples are studied, the effect is absent or very small
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Public lecture organised by Council for Defence of British Universities
Royal Holloway, University of London
Tuesday 20th November 2018
Biomarkers for psychological phenotypes?
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Data simulation basics
This document demonstrates how to simulate experimental data in Excel and R to gain insights into study design and statistical analysis. It shows how to generate random normal distributions to represent two groups, with and without an effect added, and then perform t-tests on the simulated data. Running many such simulations allows understanding of false positive rates, statistical power for different sample sizes, and other statistical properties before collecting real data. The key benefits of simulation include anticipating study design issues, clarifying optimal analysis methods, and performing power analyses to determine appropriate sample sizes.
Simulating data to gain insights into power and p-hacking
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This document discusses issues with reproducibility in EEG research and proposes solutions. It notes that flexible choices in EEG methodology and exploratory analyses can lead to false positives. Simulations demonstrate how double dipping, multiple comparisons, and lack of independent replication can produce significant effects from noise alone. The document advocates for preregistering analysis plans, including dummy effects in studies, subdividing data for exploration and replication, and using registered reports to improve reproducibility in EEG research.
What is Developmental Language Disorder
This document summarizes an international consensus on diagnosis and terminology for children with language disorders.
The consensus was reached through a Delphi approach involving 57 experts. They agreed on using the term "developmental language disorder" or DLD to identify children needing specialist help beyond what is available in the classroom. DLD is a broad category that encompasses heterogeneous impairments and overlaps with other neurodevelopmental disorders.
The experts outlined diagnostic criteria for DLD, including impaired social/educational functioning and risk factors. Key issues discussed were distinguishing delays from disorders, ensuring access to services, and the need for more intervention research to strengthen the evidence base and outcomes. The goal was to find agreed terminology to help children while minimizing negative
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Same or di...
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The document discusses responsible ways to handle mistakes in science. It examines several case studies of scientists retracting papers due to errors. Key points made include:
- Mistakes are common in science but can be avoided through open sharing of data and code
- It is important to promptly correct the record when mistakes are found to avoid others building on erroneous work
- Retracting papers for honest errors should not negatively impact scientists' careers; integrity is valued
- When others cannot replicate results, discussion and collaboration are preferable to public accusations
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4 major threats to reproducibility are publication bias, low power, p-hacking and HARKing. In this talk I explain these terms and show how study pre-registration can fix them
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Southampton: lecture on TEF
Lecture by Prof Dorothy Bishop, 1st Feb 2017, University of Southampton:
What’s wrong with our Universities, and will the Teaching Excellence Framework put it right?
Reading list: What’s wrong with our universities
This document discusses the issues with universities in the UK and whether the Teaching Excellence Framework (TEF) will help address these problems. It provides an overview of criticisms of the TEF, including that the metrics used do not properly evaluate teaching quality, the cost of implementation is high, and it could increase casualization of academic labor. Concerns are also raised about a lack of consultation on the policy and negative impacts on research. Overall, the document casts doubt on whether the TEF will successfully remedy the problems with the current university system.
IJLCD Winter Lecture 2016-7 : References
Dorothy Bishop gave a lecture on the difficulty of agreeing on definitions and terminology for children's language disorders. She discussed how different disciplines and countries use varying definitions and labels for conditions like specific language impairment. Bishop highlighted research she has conducted using the CATALISE project, a multinational Delphi consensus study, to try to establish common terminology through an expert review process. However, she noted there are still sticking points in reaching full agreement across all perspectives.
What's wrong with our Universities, and will the Teaching Excellence Framewor...
Abstract of Distinguished Lecture to be given at University of Southampton on 1st February 2017, 6 p.m.
Bishop reproducibility references nov2016
This document discusses the reproducibility crisis in science and potential solutions. It provides references from numerous papers and talks on topics like false positive findings, small sample sizes undermining reliability, questionable research practices, replication in genome-wide association studies, and unintended consequences of open research practices. The references cover pre-registration, cell line contamination issues, impact of journal impact factors, and historical examples like the discredited MMR/autism study.
More from Dorothy Bishop (20)
Open Research Practices in the Age of a Papermill Pandemic
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Language-impaired preschoolers: A follow-up into adolescence.
Language-impaired preschoolers: A follow-up into adolescence.
Otitis media with effusion: an illustration of ascertainment bias
Otitis media with effusion: an illustration of ascertainment bias
What are metrics good for? Reflections on REF and TEF
What are metrics good for? Reflections on REF and TEF
Simulating data to gain insights into power and p-hacking
Simulating data to gain insights into power and p-hacking
Developmental language disorder and auditory processing disorder:
Same or di...
Developmental language disorder and auditory processing disorder:
Same or di...
Fallibility in science: Responsible ways to handle mistakes
Fallibility in science: Responsible ways to handle mistakes
Introduction to simulating data to improve your research
Introduction to simulating data to improve your research
What's wrong with our Universities, and will the Teaching Excellence Framewor...
What's wrong with our Universities, and will the Teaching Excellence Framewor...
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∼
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19
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000
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cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
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) with
Λ
CDM. Therefore unlike low-
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Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
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truly diverge from their low-
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https://ashpc.eu/
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The shorelines of Titan’s hydrocarbon seas trace flooded erosional landforms such as river valleys; however, it isunclear whether coastal erosion has subsequently altered these shorelines. Spacecraft observations and theo-retical models suggest that wind may cause waves to form on Titan’s seas, potentially driving coastal erosion,but the observational evidence of waves is indirect, and the processes affecting shoreline evolution on Titanremain unknown. No widely accepted framework exists for using shoreline morphology to quantitatively dis-cern coastal erosion mechanisms, even on Earth, where the dominant mechanisms are known. We combinelandscape evolution models with measurements of shoreline shape on Earth to characterize how differentcoastal erosion mechanisms affect shoreline morphology. Applying this framework to Titan, we find that theshorelines of Titan’s seas are most consistent with flooded landscapes that subsequently have been eroded bywaves, rather than a uniform erosional process or no coastal erosion, particularly if wave growth saturates atfetch lengths of tens of kilometers.
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Short talk on 2 cognitive biases and reproducibility
- 1. Cognitive biases can lead to poor reproducibility and replicability of science Dorothy V. M. Bishop Professor of Developmental Neuropsychology University of Oxford @deevybee
- 2. Two cognitive biases that make it hard to do science well • Schemata: • see meaning in random noise • communicate using simplified narratives • Confirmation bias: selective attention/memory • Failure to understand probability • Misunderstand p-values; many false positives • Misunderstand sampling error: many false negatives • Asymmetric moral judgements
- 3. • Cherry-picking may not be deliberate • We find it much easier to process and remember information that agrees with our viewpoint Confirmation bias: particular problem in citations 3
- 4. 4 How common is this in practice? Even when there are trial registries, we get publication bias de Vries, Y. A., et al (2018). The cumulative effect of reporting and citation biases on the apparent efficacy of treatments: the case of depression. Psychological Medicine 48, 2453-2455. doi:10.1017/S0033291718001873
- 5. 5 How common is this in practice? Even when there are trial registries, we get publication bias de Vries, Y. A., et al (2018). The cumulative effect of reporting and citation biases on the apparent efficacy of treatments: the case of depression. Psychological Medicine 48, 2453-2455. doi:10.1017/S0033291718001873 We only know these studies exist because they are in a registry!
- 6. 6 Publication bias and reporting bias conspire to mislead us de Vries, Y. A., et al (2018). The cumulative effect of reporting and citation biases on the apparent efficacy of treatments: the case of depression. Psychological Medicine 48, 2453-2455. doi:10.1017/S0033291718001873
- 7. Inheritance of bias • When we read a peer-reviewed paper, we tend to trust the citations that back up a point • When we come to write our own paper, we cite the same materials • If prior papers only cite materials agreeing with a viewpoint, that viewpoint gets entrenched • You won’t know – unless you explicitly search – that there are other papers that give a different picture 7
- 8. 8 ADVICE TO THOSE STARTING OUT IN RESEARCH
- 9. Errors of statistical reasoning Failure to understand p-values leads to p-hacking 9
- 10. 10 I test 16 compounds to see if they improve memory in rats Should I be excited? Error bars show SE.
- 11. http://deevybee.blogspot.com/2016/01/the-amazing-significo-why-researchers.html P-hacking Simple explainer using poker Probability from unbiased deck of cards = 1 in 50 • If magician tells you he’ll deal you ‘3 of a kind’, and he does so, you should be impressed 3 of a kind • If magician deals 50 hands, and one of them is ‘3 of a kind’, you should not be impressed ‘Surprisingness’ of a result only interpretable in context of full dataset
- 12. http://deevybee.blogspot.com/2016/01/the-amazing-significo-why-researchers.html P-hacking Simple explainer using poker Probability from unbiased deck of cards = 1 in 50 • If magician tells you he’ll deal you ‘3 of a kind’, and he does so, you should be impressed 3 of a kind • If magician deals 50 hands, and one of them is ‘3 of a kind’, you should not be impressed ‘Surprisingness’ of a result only interpretable in context of full dataset
- 13. http://deevybee.blogspot.com/2016/01/the-amazing-significo-why-researchers.html P-hacking Simple explainer using poker Probability from unbiased deck of cards = 1 in 50 • If magician tells you he’ll deal you ‘3 of a kind’, and he does so, you should be impressed 3 of a kind • If magician deals 50 hands, and one of them is ‘3 of a kind’, you should not be impressed ‘Surprisingness’ of a result only interpretable in context of full dataset
- 14. 14 Key error is to treat a p-value as an indicator of importance of a finding, regardless of context I’m not testing whether J is ‘significant’ here – I’m testing whether any of 16 compounds is ‘significant’ Relevant probability is 1– (probability that all compounds are ‘nonsignificant’) = 1 - .95^16 = 0.60 Simulated data from random normal distribution with mean = 0
- 15. 15 To understand p-values: SIMULATED DATA “Just as lab scientists are not allowed to handle dangerous substances without safety training, researchers should not be allowed anywhere near a P value or similar measure of statistical probability until they have demonstrated understanding of what it means.” Bishop DVM. Nature 583, World view: How scientists can stop fooling themselves. July 23 2020
- 16. 16 Professor Dorothy Bishop, FRS, FMedSci, FBA, Department of Experimental Psychology, Anna Watts Building, Woodstock Road, Oxford, OX2 6GG. @deevybee http://deevybee.blogspot.com/2012/11/bishopblog-catalogue- updated-24th-nov.html https://www.slideshare.net/deevybishop https://orcid.org/0000-0002-2448-4033