Randomized experiments are the best way to estimate causal effects by reducing confounding variables. However, random assignment is not always possible. Natural experiments use situations where treatments are "as-if randomly" assigned, like hospitalization being more likely on certain days of the week due to staff schedules. While not perfectly random, natural experiments can provide evidence of causal effects when logistically challenging randomized experiments cannot be done. The document discusses limitations of observational studies and importance of considering counterfactuals to estimate causal effects. It also reviews challenges with randomization and potential for bias even in randomized experiments.
Sample size determination in clinical trials is considered from various ethical and practical perspectives. It is concluded that cost is a missing dimension and that the value of information is key.
Minimisation is an approach to allocating patients to treatment in clinical trials that forces a greater degree of balance than does randomisation. Here I explain why I dislike it.
Views of the role of hypothesis falsification in statistical testing do not divide as cleanly between frequentist and Bayesian views as is commonly supposed. This can be shown by considering the two major variants of the Bayesian approach to statistical inference and the two major variants of the frequentist one.
A good case can be made that the Bayesian, de Finetti, just like Popper, was a falsificationist. A thumbnail view, which is not just a caricature, of de Finetti’s theory of learning, is that your subjective probabilities are modified through experience by noticing which of your predictions are wrong, striking out the sequences that involved them and renormalising.
On the other hand, in the formal frequentist Neyman-Pearson approach to hypothesis testing, you can, if you wish, shift conventional null and alternative hypotheses, making the latter the strawman and by ‘disproving’ it, assert the former.
The frequentist, Fisher, however, at least in his approach to testing of hypotheses, seems to have taken a strong view that the null hypothesis was quite different from any other and there was a strong asymmetry on inferences that followed from the application of significance tests.
Finally, to complete a quartet, the Bayesian geophysicist Jeffreys, inspired by Broad, specifically developed his approach to significance testing in order to be able to ‘prove’ scientific laws.
By considering the controversial case of equivalence testing in clinical trials, where the object is to prove that ‘treatments’ do not differ from each other, I shall show that there are fundamental differences between ‘proving’ and falsifying a hypothesis and that this distinction does not disappear by adopting a Bayesian philosophy. I conclude that falsificationism is important for Bayesians also, although it is an open question as to whether it is enough for frequentists.
Sample size determination in clinical trials is considered from various ethical and practical perspectives. It is concluded that cost is a missing dimension and that the value of information is key.
Minimisation is an approach to allocating patients to treatment in clinical trials that forces a greater degree of balance than does randomisation. Here I explain why I dislike it.
Views of the role of hypothesis falsification in statistical testing do not divide as cleanly between frequentist and Bayesian views as is commonly supposed. This can be shown by considering the two major variants of the Bayesian approach to statistical inference and the two major variants of the frequentist one.
A good case can be made that the Bayesian, de Finetti, just like Popper, was a falsificationist. A thumbnail view, which is not just a caricature, of de Finetti’s theory of learning, is that your subjective probabilities are modified through experience by noticing which of your predictions are wrong, striking out the sequences that involved them and renormalising.
On the other hand, in the formal frequentist Neyman-Pearson approach to hypothesis testing, you can, if you wish, shift conventional null and alternative hypotheses, making the latter the strawman and by ‘disproving’ it, assert the former.
The frequentist, Fisher, however, at least in his approach to testing of hypotheses, seems to have taken a strong view that the null hypothesis was quite different from any other and there was a strong asymmetry on inferences that followed from the application of significance tests.
Finally, to complete a quartet, the Bayesian geophysicist Jeffreys, inspired by Broad, specifically developed his approach to significance testing in order to be able to ‘prove’ scientific laws.
By considering the controversial case of equivalence testing in clinical trials, where the object is to prove that ‘treatments’ do not differ from each other, I shall show that there are fundamental differences between ‘proving’ and falsifying a hypothesis and that this distinction does not disappear by adopting a Bayesian philosophy. I conclude that falsificationism is important for Bayesians also, although it is an open question as to whether it is enough for frequentists.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
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 ...
Statistics is a powerful tool for both researchers and decision makers, yet, there remains many misuse, misinterpretations, and misrepresentations of statistics. This seminar aims at raising awareness of common misconceptions in statistics in social science and beyond (e.g. media, readers). I do not own the copyrights of the materials in this presentation, all the sources were added in the bottom of the slide in which I borrowed the figures from other sources.
General Psychology Interpret an instance of behavior (individual .docxlianaalbee2qly
General Psychology
: Interpret an instance of behavior (individual or collective) recently in the news from the point of view of any two of the three schools of thought that became popular when psychology emerged as a discipline. Your response should include specific details including the major theorists and goals of the two selected schools of psychological thought. Your response should be at least 200 words in length. You are required to use at least your textbook as source material for your response. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations. Wade, C., Tavris, C., & Garry, M. (2014). Psychology (11th ed.). Upper Saddle River, NJ: Pearson Education. Must be done in APA format
ONE PAGE /275 WORDS ONE SOURCE BOOK REFERENCE
[1/29/16, 11:29 AM] josphat mungai (
[email protected]
):
Author: R.A. Noe
Employee training and development (6th ed.). New York, NY: McGraw-Hill
2:General Psychology
: A researcher hypothesizes that adults will respond differently to the same baby depending on how the child is dressed. Her colleague, on the other hand, hypothesizes that boys and girls are treated equally and that only temperamental differences lead to differences in their handling. Design a research study to test their hypotheses. Your response should be at least 200 words in length. You are required to use at least your textbook as source material for your response. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations. Wade, C., Tavris, C., & Garry, M. (2014). Psychology (11th ed.). Upper Saddle River, NJ: Pearson Education. Must be done in APA format
ONE PAGE /275 WORDS ONE SOURCE BOOK REFERENCE
[1/29/16, 11:29 AM] josphat mungai (
[email protected]
):
Author: R.A. Noe
Employee training and development (6th ed.). New York, NY: McGraw-Hill
Put to the test: as genetic screening gets cheaper and easier, it's raising questions that health-care providers aren't prepared to answer
The American Prospect, November 2010
When my children were born in the mid-1990s, new parents could already see that prenatal genetic testing was altering the terrain of pregnancy and childbirth. Growing numbers of educated women were having children at older ages, with resulting difficulties and risks. More and more parents faced challenging, deeply personal decisions about whether to engage in genetic testing and what to do if they received unfavorable results.
I remember my own anxieties when my wife, Veronica, took a blood test that searched for elevated alpha-fetoproteins, which are associated with diverse ailments ranging from spina bifida to anencephaly. The mere prospect of these rare conditions--and even the choice to undergo the tests--was surprisingly painful. At least genetic counselors and other professionals were available to help guide us.
By that point, amniocentesis had been in wide use for more than t.
The publications describes various study designs in epidemiology. These study design are tools that researchers use in order to conduct an effective research
1 A PRIMER ON CAUSALITY Marc F. Bellemare∗ IntrodVannaJoy20
1
A PRIMER ON CAUSALITY
Marc F. Bellemare∗
Introduction
This is the second of two handouts written to help students understand quantitative methods in the social
sciences. This handout is dedicated to discussing (some) of the ways in which one can identify causal
relationships in the social sciences. In keeping with the notation introduced in the handout on linear
regression, let 𝐷𝐷 be our variable of interest; 𝑦𝑦 be an outcome of interest; and the vector 𝑥𝑥 = (𝑥𝑥1, … , 𝑥𝑥𝐾𝐾)
represent other factors – or control variables – for which we have data. For the purposes of this discussion,
let 𝐷𝐷 measure a given policy, 𝑦𝑦 measure welfare, and the vector 𝑥𝑥 measure the various control variables the
researcher has seen fit to include. See my “A Primer on Linear Regression” for a more basic handout.
Mechanics
Recall that the regression of 𝑦𝑦 on (𝐷𝐷, 𝑥𝑥1, … , 𝑥𝑥𝐾𝐾) is written as
𝑦𝑦𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝑥𝑥1𝑖𝑖 + ⋯ + 𝛽𝛽𝐾𝐾𝑥𝑥𝐾𝐾𝑖𝑖 + 𝛾𝛾𝐷𝐷𝑖𝑖 + 𝜖𝜖𝑖𝑖, (1)
where i denotes a unit of observation. In the example of wages and education, the unit of observation would
be an individual, but units of observations can be individuals, households, plots, firms, villages, communities,
countries, etc. Just as the research question should drive the choice of what to measure for 𝑦𝑦, 𝐷𝐷, and 𝑥𝑥, the
research question also drives the choice of the relevant unit of observation.
The problem is that unless the researcher runs an experiment in which she randomly assigns the level of 𝐷𝐷 to
each unit of observation i, the relationship from 𝐷𝐷 to 𝑦𝑦 will not be causal. That is, 𝛾𝛾 will not truly capture the
impact of 𝐷𝐷 on 𝑦𝑦, as it will be “contaminated” by the presence of unobservable factors. Some of those factors
can be included in 𝑥𝑥 = (𝑥𝑥1, … , 𝑥𝑥𝐾𝐾), of course, but it is in general impossible to fully control for every relevant
factor. This is especially true when unobservable or costly to observe factors (e.g., risk aversion, technical
ability, soil quality, etc.) play an important role in determining 𝐷𝐷 and 𝑦𝑦. So even if we get an estimate of 𝛾𝛾
that is statistically significant, we cannot necessarily assume that the relationship between the variable of
interest and the outcome variable is causal. In other words, correlation does not imply causation.
For example, suppose 𝐷𝐷 is an individual’s consumption of orange juice and 𝑦𝑦 is (some) indicator of health.
We have often discussed in lecture how a simple regression of 𝑦𝑦 to 𝐷𝐷 would provide us with a biased
estimate of 𝛾𝛾 because orange juice consumption is nonrandom and not exogenous to health. That is, there
are factors other than orange juice consumption which determine health. Some are observable (e.g., how
much someone exercises; whether they smoke; their diet; etc.), but several are unobservable (e.g., their
willingness to pay for orange juice; their subjective valuation of health; th
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
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 ...
Statistics is a powerful tool for both researchers and decision makers, yet, there remains many misuse, misinterpretations, and misrepresentations of statistics. This seminar aims at raising awareness of common misconceptions in statistics in social science and beyond (e.g. media, readers). I do not own the copyrights of the materials in this presentation, all the sources were added in the bottom of the slide in which I borrowed the figures from other sources.
General Psychology Interpret an instance of behavior (individual .docxlianaalbee2qly
General Psychology
: Interpret an instance of behavior (individual or collective) recently in the news from the point of view of any two of the three schools of thought that became popular when psychology emerged as a discipline. Your response should include specific details including the major theorists and goals of the two selected schools of psychological thought. Your response should be at least 200 words in length. You are required to use at least your textbook as source material for your response. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations. Wade, C., Tavris, C., & Garry, M. (2014). Psychology (11th ed.). Upper Saddle River, NJ: Pearson Education. Must be done in APA format
ONE PAGE /275 WORDS ONE SOURCE BOOK REFERENCE
[1/29/16, 11:29 AM] josphat mungai (
[email protected]
):
Author: R.A. Noe
Employee training and development (6th ed.). New York, NY: McGraw-Hill
2:General Psychology
: A researcher hypothesizes that adults will respond differently to the same baby depending on how the child is dressed. Her colleague, on the other hand, hypothesizes that boys and girls are treated equally and that only temperamental differences lead to differences in their handling. Design a research study to test their hypotheses. Your response should be at least 200 words in length. You are required to use at least your textbook as source material for your response. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying citations. Wade, C., Tavris, C., & Garry, M. (2014). Psychology (11th ed.). Upper Saddle River, NJ: Pearson Education. Must be done in APA format
ONE PAGE /275 WORDS ONE SOURCE BOOK REFERENCE
[1/29/16, 11:29 AM] josphat mungai (
[email protected]
):
Author: R.A. Noe
Employee training and development (6th ed.). New York, NY: McGraw-Hill
Put to the test: as genetic screening gets cheaper and easier, it's raising questions that health-care providers aren't prepared to answer
The American Prospect, November 2010
When my children were born in the mid-1990s, new parents could already see that prenatal genetic testing was altering the terrain of pregnancy and childbirth. Growing numbers of educated women were having children at older ages, with resulting difficulties and risks. More and more parents faced challenging, deeply personal decisions about whether to engage in genetic testing and what to do if they received unfavorable results.
I remember my own anxieties when my wife, Veronica, took a blood test that searched for elevated alpha-fetoproteins, which are associated with diverse ailments ranging from spina bifida to anencephaly. The mere prospect of these rare conditions--and even the choice to undergo the tests--was surprisingly painful. At least genetic counselors and other professionals were available to help guide us.
By that point, amniocentesis had been in wide use for more than t.
The publications describes various study designs in epidemiology. These study design are tools that researchers use in order to conduct an effective research
1 A PRIMER ON CAUSALITY Marc F. Bellemare∗ IntrodVannaJoy20
1
A PRIMER ON CAUSALITY
Marc F. Bellemare∗
Introduction
This is the second of two handouts written to help students understand quantitative methods in the social
sciences. This handout is dedicated to discussing (some) of the ways in which one can identify causal
relationships in the social sciences. In keeping with the notation introduced in the handout on linear
regression, let 𝐷𝐷 be our variable of interest; 𝑦𝑦 be an outcome of interest; and the vector 𝑥𝑥 = (𝑥𝑥1, … , 𝑥𝑥𝐾𝐾)
represent other factors – or control variables – for which we have data. For the purposes of this discussion,
let 𝐷𝐷 measure a given policy, 𝑦𝑦 measure welfare, and the vector 𝑥𝑥 measure the various control variables the
researcher has seen fit to include. See my “A Primer on Linear Regression” for a more basic handout.
Mechanics
Recall that the regression of 𝑦𝑦 on (𝐷𝐷, 𝑥𝑥1, … , 𝑥𝑥𝐾𝐾) is written as
𝑦𝑦𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝑥𝑥1𝑖𝑖 + ⋯ + 𝛽𝛽𝐾𝐾𝑥𝑥𝐾𝐾𝑖𝑖 + 𝛾𝛾𝐷𝐷𝑖𝑖 + 𝜖𝜖𝑖𝑖, (1)
where i denotes a unit of observation. In the example of wages and education, the unit of observation would
be an individual, but units of observations can be individuals, households, plots, firms, villages, communities,
countries, etc. Just as the research question should drive the choice of what to measure for 𝑦𝑦, 𝐷𝐷, and 𝑥𝑥, the
research question also drives the choice of the relevant unit of observation.
The problem is that unless the researcher runs an experiment in which she randomly assigns the level of 𝐷𝐷 to
each unit of observation i, the relationship from 𝐷𝐷 to 𝑦𝑦 will not be causal. That is, 𝛾𝛾 will not truly capture the
impact of 𝐷𝐷 on 𝑦𝑦, as it will be “contaminated” by the presence of unobservable factors. Some of those factors
can be included in 𝑥𝑥 = (𝑥𝑥1, … , 𝑥𝑥𝐾𝐾), of course, but it is in general impossible to fully control for every relevant
factor. This is especially true when unobservable or costly to observe factors (e.g., risk aversion, technical
ability, soil quality, etc.) play an important role in determining 𝐷𝐷 and 𝑦𝑦. So even if we get an estimate of 𝛾𝛾
that is statistically significant, we cannot necessarily assume that the relationship between the variable of
interest and the outcome variable is causal. In other words, correlation does not imply causation.
For example, suppose 𝐷𝐷 is an individual’s consumption of orange juice and 𝑦𝑦 is (some) indicator of health.
We have often discussed in lecture how a simple regression of 𝑦𝑦 to 𝐷𝐷 would provide us with a biased
estimate of 𝛾𝛾 because orange juice consumption is nonrandom and not exogenous to health. That is, there
are factors other than orange juice consumption which determine health. Some are observable (e.g., how
much someone exercises; whether they smoke; their diet; etc.), but several are unobservable (e.g., their
willingness to pay for orange juice; their subjective valuation of health; th
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
2. Example: Hospitalization on health
What’s wrong with estimating this model from observational data?
Health
tomorrow
Hospital
visit today Effect?
Arrow means “X causes Y”
3. Confounds
The effect and cause might be
confounded by a common cause,
and be changing together as a
result
Health
tomorrow
Hospital
visit today Effect?
Health
today
Dashed circle means “unobserved”
4. Confounds
If we only get to observe them
changing together, we can’t
estimate the effect of
hospitalization changing alone
Health
tomorrow
Hospital
visit today Effect?
Health
today
5. “To find out what happens when you change
something, it is necessary to change it.”
-GEORGE BOX
6. Hospital
visit today
Random assignment
Random assignment determines the treatment independent of any
confounds
Health
tomorrowEffect?
Health
today
Coin flip
Double lines mean
“intervention”
7. Counterfactuals
To isolate the causal effect, we have to change one and only one
thing (hospital visits), and compare outcomes
+ vs
(what happened)
Reality
(what would have happened)
Counterfactual
8. Counterfactuals
We never get to observe what would have happened if we did
something else, so we have to estimate it
+ vs
(what happened)
Reality
(what would have happened)
Counterfactual
9. Random assignment
We can use randomization to create two groups that differ only in
which treatment they receive, restoring symmetry
+
World 1 World 2
Heads Tails
10. Random assignment
We can use randomization to create two groups that differ only in
which treatment they receive, restoring symmetry
+
World 1 World 2
Heads Tails
11. Random assignment
We can use randomization to create two groups that differ only in
which treatment they receive, restoring symmetry
+
World 1 World 2
13. Problems
Random assignment is the “gold standard” for causal inference, but
can be misleading under certain circumstances
◦ Small sample sizes
◦ Researcher degrees of freedom
◦ Publication bias
◦ P-hacking
16. Electronic copy available at: https://ssrn.com/abstract=1850704
designed to demonstrate something false: that certain songs
can change listeners’ age. Everything reported here actually
happened.1
Study 1:musical contrast and subjective age
In Study 1, we investigated whether listening to a children’s
song induces an age contrast, making people feel older. In
exchange for payment, 30 University of Pennsylvania under-
graduates sat at computer terminals, donned headphones, and
were randomly assigned to listen to either a control song
(“Kalimba,” an instrumental song by Mr. Scruff that comes
free with the Windows 7 operating system) or a children’s
song (“Hot Potato,” performed by The Wiggles).
After listening to part of the song, participants com-
pleted an ostensibly unrelated survey: They answered the
question “How old do you feel right now?” by choosing
among five options (very young, young, neither young nor
old, old, and very old). They also reported their father’s
age, allowing us to control for variation in baseline age
across participants.
An analysis of covariance (ANCOVA) revealed the pre-
dicted effect: People felt older after listening to “Hot Potato”
tematic analysis of how researcher degrees of freedom influ-
ence statistical significance. Impatient readers can consult
Table 3.
“How Bad Can It Be?” Simulations
Simulationsof common researcher degreesof
freedom
We used computer simulations of experimental data to esti-
mate how researcher degrees of freedom influence the proba-
bility of a false-positive result. These simulations assessed
the impact of four common degrees of freedom: flexibility in
(a) choosing among dependent variables, (b) choosing sample
size, (c) using covariates, and (d) reporting subsets of experi-
mental conditions. We also investigated various combinations
of these degrees of freedom.
We generated random samples with each observation inde-
pendently drawn from a normal distribution, performed sets of
analyses on each sample, and observed how often at least one
of the resulting p values in each sample was below standard
significance levels. For example, imagine a researcher who
collects two dependent variables, say liking and willingness to
by guest on November 20, 2011pss.sagepub.comDownloaded from
1360 Simmonset al.
of ambiguous information and remarkably adept at reaching
justifiable conclusions that mesh with their desires (Babcock
& Loewenstein, 1997; Dawson, Gilovich, & Regan, 2002;
Gilovich, 1983; Hastorf & Cantril, 1954; Kunda, 1990; Zuck-
erman, 1979). This literature suggests that when we as
researchers face ambiguous analytic decisions, we will tend to
conclude, with convincing self-justification, that the appropri-
ate decisions are those that result in statistical significance
(p ≤ .05).
Ambiguity is rampant in empirical research. As an exam-
ple, consider a very simple decision faced by researchers ana-
lyzing reaction times: how to treat outliers. In a perusal of
roughly 30 Psychological Science articles, we discovered con-
siderable inconsistency in, and hence considerable ambiguity
about, this decision. Most (but not all) researchers excluded
some responses for being too fast, but what constituted “too
fast” varied enormously: the fastest 2.5%, or faster than 2 stan-
dard deviations from the mean, or faster than 100 or 150 or
200 or 300 ms. Similarly, what constituted “too slow” varied
enormously: the slowest 2.5% or 10%, or 2 or 2.5 or 3 stan-
dard deviations slower than the mean, or 1.5 standard devia-
tions slower from that condition’s mean, or slower than 1,000
or 1,200 or 1,500 or 2,000 or 3,000 or 5,000 ms. None of these
decisions is necessarily incorrect, but that fact makes any of
them justifiable and hence potential fodder for self-serving
(adjusted M = 2.54 years) than after listening to the control
song (adjusted M = 2.06 years), F(1, 27) = 5.06, p = .033.
In Study 2, we sought to conceptually replicate and extend
Study 1. Having demonstrated that listening to a children’s
song makes people feel older, Study 2 investigated whether
listening to a song about older age makes people actually
younger.
Study 2:musical contrast and chronological
rejuvenation
Using the same method as in Study 1, we asked 20 University
of Pennsylvania undergraduates to listen to either “When I’m
Sixty-Four” by The Beatles or “Kalimba.” Then, in an ostensi-
bly unrelated task, they indicated their birth date (mm/dd/
yyyy) and their father’s age. We used father’s age to control
for variation in baseline age across participants.
An ANCOVA revealed the predicted effect: According to
their birth dates, people were nearly a year-and-a-half younger
after listening to “When I’m Sixty-Four” (adjusted M = 20.1
years) rather than to “Kalimba” (adjusted M = 21.5 years),
F(1, 17) = 4.92, p = .040.
Discussion
18. False-Positive Psychology 1361
Table 1. Likelihood of ObtainingaFalse-Positive Result
Significance level
Researcher degrees of freedom p < .1 p < .05 p < .01
Situation A:two dependent variables (r = .50) 17.8% 9.5% 2.2%
Situation B: addition of 10 more observations
per cell
14.5% 7.7% 1.6%
Situation C: controllingfor gender or interaction
of gender with treatment
21.6% 11.7% 2.7%
Situation D: dropping(or not dropping) one of
three conditions
23.2% 12.6% 2.8%
Combine Situations A and B 26.0% 14.4% 3.3%
Combine Situations A,B,and C 50.9% 30.9% 8.4%
Combine Situations A,B,C,and D 81.5% 60.7% 21.5%
Note: The table reportsthe percentage of 15,000 simulated samplesin which at least one of a
set of analyseswassignificant. Observationswere drawn independently from anormal distribu-
tion.Baseline isatwo-condition design with 20 observationsper cell.Resultsfor SituationA were
obtained by conductingthree t tests,one on each of two dependent variablesand athird on the
average of these two variables.Resultsfor Situation B were obtained by conductingone t test after
collecting20 observationsper cell and another after collectingan additional 10 observationsper
cell.Resultsfor Situation C were obtained by conductinga t test,an analysisof covariance with a
19.
20. Caveats / limitations
Random assignment is the “gold standard” for causal inference, but
it has some limitations:
◦ Randomization often isn’t feasible and/or ethical
◦ Experiments are costly in terms of time and money
◦ It’s difficult to create convincing parallel worlds
◦ Inevitably people deviate from their random assignments
Anyone can flip a coin, but it’s difficult to create convincing parallel
worlds
25. Natural experiments
Sometimes we get lucky and nature effectively runs experiments for
us, e.g.:
◦ As-if random: People are randomly exposed to water sources
◦ Instrumental variables: A lottery influences military service
◦ Discontinuities: Star ratings get arbitrarily rounded
◦ Difference in differences: Minimum wage changes in just one state
26. Natural experiments
Sometimes we get lucky and nature effectively runs experiments for
us, e.g.:
◦ As-if random: People are randomly exposed to water sources
◦ Instrumental variables: A lottery influences military service
◦ Discontinuities: Star ratings get arbitrarily rounded
◦ Difference in differences: Minimum wage changes in just one state
Experiments happen all the time, we just have to notice them
27. As-if random
Idea: Nature randomly assigns
conditions
Example: People are randomly
exposed to water sources (Snow,
1854)
http://bit.ly/johnsnowmap
30. Figure 4: Average Revenue around Discontinuous Changes in Rating
Notes: Each restaurant’s log revenue is de-meaned to normalize a restaurant’s average log
revenue to zero. Normalized log revenues are then averaged within bins based on how far the
restaurant’s rating is from a rounding threshold in that quarter. The graph plots average log
revenue as a function of how far the rating is from a rounding threshold. All points with a
positive (negative) distance from a discontinuity are rounded up (down).
Regression
discontinuities
Idea: Things change around an
arbitrarily chosen threshold
Example: Star ratings get
arbitrarily rounded (Luca, 2011)
http://bit.ly/yelpstars
31. Difference in
differences
Idea: Compare differences after a
sudden change with trends in a
control group
Example: Minimum wage changes
in just one state (Card & Krueger,
1994)
http://stats.stackexchange.com/a/125266
32. Natural experiments: Caveats
Natural experiments are great, but:
◦ Good natural experiments are hard to find
◦ They rely on many (untestable) assumptions
◦ The treated population may not be the one of interest
33. Natural experiments: Caveats
Natural experiments are great, but:
◦ Good natural experiments are hard to find
◦ They rely on many (untestable) assumptions
◦ The treated population may not be the one of interest
Sometimes we can use additional data + algorithms to
automatically find natural experiments
48. Confound: Correlated demand
Some views would have
happened anyway due to
correlated demand
We call these convenience clicks
Direct
views of
hats
Referred
click-
throughs
to gloves
Effect?
Demand
for winter
items
49. Ideally we would run an A/B test where randomly selected people
see recommendations
Ideal experiment
World 1 World 2
50. Natural experiment
Instead, we can exploits sudden shocks in traffic to focal products as
an instrumental variable
Direct
views of
focal
product
Referred
click-
throughsEffect?
Demand
External
shock
51. Usual
approach
Think hard for a source
of random variation that
only directly affects focal
product (e.g., author
wins award)
52. Usual
approach
Think hard for a source
of random variation that
only directly affects focal
product (e.g., author
wins award)
Problem: Impossible to
rule out side effects
53. New approach: Automatically discovering
natural experiments
Look for products that receive shocks in direct traffic, while their
recommendations do not
54. New approach: Automatically discovering
natural experiments
Look for products that receive shocks in direct traffic, while their
recommendations do not
55. New approach: Automatically discovering
natural experiments
Applying this method to 23 million pageviews in the Bing Toolbar
logs, we find over 4,000 such natural experiments
56. New approach: Automatically discovering
natural experiments
Causal click-through rate is just the marginal gain in clicks during the
shock
Causal effect = Change in recommendation clicks / Size of shock
57. Causal click-through rate is just the
marginal gain in clicks during the
shock
Causal effect = Change in rec clicks /
Size of shock
Results
58. Although recommendation click-
throughs account for a large fraction
of traffic, at least 75% of this activity
would likely occur in the absence of
recommendations*
Results
*With lots of caveats