Chapter 1 The Big (Bayesian) Picture
How can we live if we don’t change?
- Beyoncé. Lyric from Satellites.
Everybody changes their mind. You likely even changed your mind in the last minute. Prior to ever opening it, you no doubt had some preconceptions about this book, whether formed from its title, its online reviews, or a conversation with a friend who has read it. And then you saw that the first chapter opened with a quote from Beyoncé, an unusual choice for a statistics book. Perhaps this made you think ‘This book is going to be even more fun than I realized!’ Perhaps it served to do the opposite. No matter. The point we want to make is that we agree with Beyoncé – changing is simply part of life.
As humans, we continuously update our knowledge about the world as we accumulate lived experiences, or collect data. As children, it takes (hopefully) only one run-in with a hot stove to understand that hot things hurt. Or a couple of attempts at conversation to understand that, unlike in cartoons, real dogs can’t talk. Other knowledge is longer in the making. For example, suppose there’s a new Italian restaurant in your neighborhood. It has a 5-star online rating! And you happen to love Italian food! Thus prior to ever stepping foot in the restaurant, you anticipate that the food will be quite delicious. On your first trip to the restaurant, you collect some edible data: your order for pasta al dente arrives a soggy mess. Weighing the stellar online rating against your own terrible meal (which might have just been a fluke), you update your information: you view this as a 3-star not 5-star restaurant. Willing to give the restaurant another chance, you make a second trip. On this visit, you’re pleased with your Alfredo dish and increase the restaurant’s rating to 4 stars. You continue to visit the restaurant, collecting edible data and updating your knowledge each time. Figure 1.1 captures the natural Bayesian knowledge building process of acknowledging your preconceptions, using data to update your knowledge, and repeating.
We can apply this same Bayesian process to rigorous research inquiries. If you’re a political scientist, yours might be a study of demographic factors in voting patterns. If you’re an environmental scientist, yours might be an analysis of the human role in climate change. You don’t walk into such an inquiry without context - you carry a degree of incoming or prior information based on previous research and experience. Naturally, it’s in light of this information that you interpret new data, weighing both in developing your updated or posterior information. You continue to refine this information as you gather new evidence:
The Bayesian philosophy represented in Figure 1.2 is the foundation of Bayesian statistics. Throughout this book, you will build the methodology and tools you need to implement this philosophy in a rigorous data analysis. This experience will require a sense of purpose and a map.
- Learn to think like a Bayesian;
- Explore the foundations of a Bayesian data analysis and how they contrast with the frequentist alternative; and
- Learn a little bit about the history of the Bayesian philosophy.
1.1 Thinking like a Bayesian
Given our emphasis on how natural the Bayesian approach to knowledge building is, you might be surprised to know that the alternative frequentist philosophy has traditionally dominated statistics. Before exploring their differences, it’s important to note that Bayesian and frequentist analyses share a common goal: to learn from data about the world around us. Both Bayesians and frequentists use data to fit models, make predictions, and evaluate hypotheses. Moreover, when working with the same data, they will typically arrive at a similar set of conclusions. Yet there are key differences in the logic behind, approach to, and interpretation of these conclusions.
1.1.1 Quiz yourself
Before we elaborate upon the Bayesian vs frequentist debate, take a quick quiz to assess your current inclinations. In doing so, try to abandon any rules that you might have learned in the past and just go with your gut.
When flipping a fair coin, we say that ‘the probability of flipping Heads is 0.5.’ How do you interpret this probability?
- If I flip this coin over and over, roughly 50% will be Heads.
- Heads and Tails are equally plausible.
- Both a and b make sense.
An election is coming up and a pollster claims that ‘candidate A has a 0.9 probability of winning.’ How do you interpret this probability?
- If we observe the election over and over, candidate A will win roughly 90% of the time.
- Candidate A is much more likely to win than to lose.
- The pollster’s calculation is wrong. Candidate A will either win or lose, thus their probability of winning can only be 0 or 1.
Consider two claims. (1) Zuofu claims that he can predict the outcome of a coin flip. To test his claim, you flip a fair coin 10 times and he correctly predicts all 10! (2) Kavya claims that she can distinguish natural and artificial sweeteners. To test her claim, you give her 10 sweetener samples and she correctly identifies each! In light of these experiments, what do you conclude?
- You’re more confident in Kavya’s claim than Zuofu’s claim.
- The evidence supporting Zuofu’s claim is just as strong as the evidence supporting Kavya’s claim.
Suppose that during a recent doctor’s visit, you tested positive for a very rare disease. If you only get to ask the doctor one question, which would it be?
- What’s the chance that I actually have the disease?
- If in fact I don’t have the disease, what’s the chance that I would’ve gotten this positive test result?
Next, tally up your quiz score using the scoring system below.1 Totals from 4-5 indicate that your current thinking is fairly frequentist whereas totals from 9-12 indicate you already think like a Bayesian. In between these extremes, totals from 6-8 indicate that you’re not currently taking sides. Your current inclinations might be more frequentist than Bayesian or vice versa. These inclinations might change throughout your reading of this book. They might not. For now, we merely wish to highlight the key differences between the Bayesian and frequentist philosophies. For brevity, we refer to people applying these philosophies as “Bayesians” and “frequentists,” yet the distinction is not so clear in practice – even the authors apply both philosophies in their work.
1.1.2 The meaning of probability
Probability theory is central to every statistical analysis. Yet, as illustrated by questions 1 and 2 in Section 1.1.1, Bayesians and frequentists differ on something as fundamental as the meaning of “probability.” For example, in question 1, a Bayesian and frequentist would both say that the probability of observing Heads on a fair coin flip is 1/2. The difference is in their interpretation.
- To Bayesians, a probability measures the relative plausibility of an event.
- Frequentists are so named for their interpretation of probability as the long-run relative frequency of a repeatable event.
Thus in the coin flip example, a Bayesian would conclude that Heads and Tails are equally likely. In contrast, a frequentist would conclude that if we flip the coin over and over and over, roughly 1/2 of these flips will be Heads. Let’s try applying these same ideas to the question 2 setting in which a pollster declares that candidate A has a 0.9 probability of winning the upcoming election. This routine election calculation illustrates cracks within the frequentist interpretation. Since the election is a one time event, the long-run relative frequency concept of observing the election over and over simply doesn’t apply. A very strict frequentist interpretation might even conclude the pollster is just wrong. Since the candidate will either win or lose, their win probability must be either 1 or 0. A less extreme frequentist interpretation, though a bit awkward, is more reasonable: in long-run hypothetical repetitions of the election, i.e. elections with similar circumstances, candidate A would win roughly 90% of the time.
The election example is not rare. It’s often the case that an event of interest is unrepeatable. Whether or not a politician wins an election, whether or not it rains tomorrow, and whether or not humans will live on Mars are all one-time events. Whereas the frequentist interpretation of probability is awkward in these one-time settings, the more flexible Bayesian interpretation provides a path by which to express the uncertainty of these events. For example, a Bayesian would interpret “a 90% chance of winning” to mean that, based on election models, the relative plausibility of winning is high – the candidate is much more likely (specifically, 9 times more likely) to win than to lose.
1.1.3 The Bayesian balancing act
Inspired by an example by Berger (1984), question 3 in Section 1.1.1 presented you with two scenarios: (1) Zuofu claims that he can predict the outcome of a coin flip; and (2) Kavya claims that she can distinguish between natural and artificial sweeteners. Let’s agree here that the first claim is simply ridiculous but that the second is plausible (some people have sensitive palates!). Thus imagine our surprise when, in testing their claims, both Zuofu and Kavya enjoyed a 10 out of 10 success rate: Zuofu correctly predicted the outcomes of 10 coin flips and Kavya correctly identified the source of 10 different sweeteners. What can we conclude from this data? Does it provide equal evidence in support of both Zuofu’s and Kavya’s claims? Just as they don’t agree on the fundamental meaning of probability, Bayesians and frequentists answer these questions through different lenses.
Let’s begin by looking through the frequentist lens which, to oversimplify a bit, looks at the data without the surrounding context. Thus in a frequentist analysis, “10 out of 10” is “10 out of 10” no matter if it’s in the context of Zuofu’s coins or Kavya’s sweeteners. This means that a frequentist would be equally confident that Zuofu can predict coin flips and Kavya can distinguish between natural and artificial sweeteners (at least on paper if not in their gut).2 Given the absurdity of Zuofu’s claim, this frequentist conclusion is a bit bizarre – it throws out all prior knowledge in favor of a mere 10 data points. We can’t resist representing this conclusion with Figure 1.3, a frequentist complement to the Bayesian knowledge building diagram in Figure 1.2, which solely consists of the data.
In contrast, a Bayesian analysis gives voice to our prior knowledge. Here, our experience on Earth suggests that Zuofu is probably overstating his abilities but that Kavya’s claim is reasonable. Thus after weighing their equivalent “10 out of 10” achievements against these different priors, our posterior understanding of Zuofu’s and Kavya’s claims differ. We’re even more certain that Kavya is a sweetener savant (the data is consistent with our prior). However, given its inconsistency with our prior experience, we are chalking Zuofu’s “psychic” achievement up to simple luck.
The idea of allowing one’s prior experience to play a formal role in a statistical analysis might seem a bit goofy. In fact, a common frequentist critique of the Bayesian philosophy is that it’s too subjective. We haven’t done much to combat this critique yet. Favoring flavor over details, Figure 1.2 might even lead you to believe that Bayesian analysis involves a bit of subjective hocus pocus: combine your prior with some data and poof, out pops your posterior. In reality, the Bayesian philosophy provides a formal framework for such knowledge creation. This framework depends upon prior information, data, and the balance between them (Figure 1.4).
In building the posterior, the balance between the prior information and data is determined by the relative strength of each. For example, we had a very strong prior understanding that Zuofu isn’t a psychic, yet very little data (10 coin flips) supporting his claim. Thus, like the left plot in Figure 1.4, the prior held more weight in our posterior understanding. However, we’re not stubborn. If Zuofu had correctly predicted the outcome of, say, 10,000 coin flips, the strength of this data would far surpass that of our prior, leading to a posterior conclusion that perhaps Zuofu is psychic after all (like the right plot in Figure 1.4)!
Allowing the posterior to balance out the prior and data is critical to the Bayesian knowledge building process. When we have little data, our posterior can draw upon the power in our prior knowledge. As we collect more data, the prior loses its influence. Whether in science, policy-making, or life, this is how people tend to think (El-Gamal and Grether 1995) and how progress is made. As they collect more and more data, two scientists will come to agreement on the human role in climate change, no matter their prior training and experience.3 As they read more and more pages of this book, two readers will come to agreement on the power of Bayesian statistics! This logical and heartening idea is illustrated by Figure 1.5.
1.1.4 Asking questions
In question 4 of Section 1.1.1, you were asked to imagine that you tested positive for a rare disease and only got to ask the doctor one question: (a) what’s the chance that I actually have the disease?; or (b) if in fact I do not have the disease, what’s the chance that I would’ve gotten this positive test result? The authors are of the opinion that, though the answers to both questions would be helpful, we’d rather know the answer to (a). That is, we’d rather understand the uncertainty in our unknown disease status than in our observed test result. Unsurprising spoiler: though Bayesians and frequentists share the goal of using your test results (the data) to assess whether you have the rare disease (the hypothesis), a Bayesian analysis would answer (a) whereas a frequentist analysis would answer (b). Specifically, a Bayesian analysis assesses the uncertainty of the hypothesis in light of the observed data, and a frequentist analysis assesses the uncertainty of the observed data in light of an assumed hypothesis.
In a hypothesis test…
- a Bayesian asks: in light of the observed data, what’s the chance that the hypothesis is correct?
- a frequentist asks: if in fact the hypothesis is incorrect, what’s the chance I’d have observed this, or even more extreme, data?
For clarity, consider the scenario summarized in Table 1.1 where, in a population of 100, only four people have the disease. Among the 96 without the disease, nine test positive thus get misleading test results. Among the four with the disease, three test positive thus get accurate test results.
|test positive||test negative||total|
In this scenario, a Bayesian would ask: given my positive test result, what’s the chance that I actually have the disease? Since only 3 of the 12 people that tested positive have the disease (Table 1.1), there’s only a 25% chance that you have the disease. Thus when we take into account the disease’s rarity and the relatively high false positive rate, it’s relatively unlikely that you actually have the disease. What a relief.
Recalling Section 1.1.2, you might anticipate that a frequentist would take a different approach to this analysis. From the frequentist standpoint, since disease status isn’t repeatable, the probability you have the disease is either 1 or 0 – you have it or you don’t. To the contrary, medical testing (and data collection in general) is repeatable. You can get tested for the disease over and over and over. Thus a frequentist would ask: if I don’t actually have the disease, what’s the chance that I would’ve tested positive? Since only 9 of the 96 people without the disease tested positive, there’s a roughly 10% (9/96) chance that you would’ve tested positive even if you didn’t have the disease.
The 9/96 frequentist probability calculation is similar in spirit to a p-value. In general, p-values measure the chance of having observed data as or more extreme than ours if in fact our original hypothesis is incorrect. Though the p-value was prominent in the frequentist practice for decades, it’s slowly being de-emphasized by both frequentists and Bayesians. Essentially, it’s so commonly misinterpreted and misused (Goodman 2011), that the American Statistical Association put out an official ‘public safety announcement’ regarding its usage (Wasserstein 2016). The reason the p-value is so commonly misinterpreted is simple - it’s more natural to study the uncertainty of a yet-unproven hypothesis (whether you have the rare disease) than the uncertainty of data we have already observed (you tested positive for the rare disease).4
1.2 A quick history lesson
Given how natural Bayesian thinking is, you might be surprised to know that Bayes’ momentum is relatively recent. Once an obscure term outside specialized industry and research circles, “Bayesian” has popped up on TV shows (eg: The Big Bang Theory5 and Numb3rs6), has been popularized by various blogs (eg: FiveThirtyEight), and regularly appears in the media (eg: the New York Times’ explanation of how to think like an epidemiologist (Roberts 2020)).
Despite its recent rise in popularity, Bayesian statistics is rooted in the eighteenth century work of Reverend Thomas Bayes, a statistician, minister, and philosopher. Though Bayes developed his philosophy during the 1740’s, it wasn’t until the late twentieth century that this work reached a broad audience. During the more than two centuries in between, the frequentist philosophy dominated statistical research and practice. The Bayesian philosophy not only fell out of popular favor during this time, it was stigmatized. As recently as 1990, Marilyn vos Savant was ridiculed by readers of her Parade magazine column when she presented a Bayesian solution to the now classic Monty Hall probability puzzle. Apparently more than 10,000 readers wrote in to dispute and mock her solution7. Vos Savant’s Bayesian solution was later proven to be indisputably correct. Yet with this level of scrutiny, it’s no wonder that many researchers kept their Bayesian pursuits under wraps. Though neither proclaimed as much at the time, Alan Turing cracked Germany’s ‘Enigma’ code in World War II and John Tukey pioneered election-day predictions in the 1960s using Bayesian methods (McGrayne 2012).
As the public existence of this book suggests, the stigma has largely eroded. Why? (1) Advances in computing. Bayesian applications require sophisticated computing resources that weren’t broadly available until the 1990s. (2) Departure from tradition. Due to the fact that it’s typically what people learn, frequentist methods are ingrained in practice. Due to the fact that it’s often what people use in practice, frequentist methods are ingrained in the statistics curriculum, thus are what people learn. This cycle is difficult to break. (3) Reevaluation of “subjectivity.” Modern statistical practice is a child of the Enlightenment. A reflection of the Enlightenment ideals, frequentist methods were embraced as the superior, objective alternative to the subjective Bayesian philosophy. This subjective stigma is slowly fading for several reasons. First, the “subjective” label can be stamped on all statistical analyses, whether frequentist or Bayesian. Our prior knowledge naturally informs what we measure, why we measure it, and how we model it. Second, post-Enlightenment, “subjective” is no longer such a dirty word. After all, just as two bakers might use two different recipes to produce equally tasty bagels, two analysts might use two different techniques to produce equally informative analyses.
Figure 1.6 provides a sense of scale for the Bayesian timeline. Remember that Thomas Bayes (left) started developing the Bayesian philosophy in the 1740’s. It wasn’t until 1969, more than 200 years later (!), that David Blackwell (center) introduced one of the first Bayesian textbooks (Blackwell 1969). The Bayesian community is now rapidly expanding around the globe,8 with the Bayesian framework being used for everything from modeling COVID-19 rates in South Africa (Mbuvha and Marwala 2020) to monitoring human rights violations (Lum et al. 2010). Not only are we, this book’s authors, part of this Bayesian story now, we hope to have created a resource that invites you (right) to be a part of this story too!
1.3 A look ahead
Throughout this book, you will learn how to bring your Bayesian thinking to life. The structure for this exploration is outlined below. Though the chapters are divided into broader units which each have a unique theme, there is a common thread throughout the book: building and analyzing Bayesian models for the behavior of some variable “\(Y\).”
1.3.1 Unit 1: Bayesian foundations
How can we incorporate Bayesian thinking into a formal model of the behavior in \(Y\), some variable of interest?
Unit 1 focuses on developing a foundation upon which to build our Bayesian analyses. You will explore the heart of every Bayesian model, Bayes’ Rule, and put Bayes’ Rule into action to build a few introductory but fundamental Bayesian models. These models are all unique, but tied together in the broader conjugate family. Further, they are tailored toward variables \(Y\) of differing structures, thus allow us to apply our Bayesian thinking in a wide variety of scenarios.
To begin, the Beta-Binomial model can help us determine the probability that it rains tomorrow in Australia using data on binary categorical variable \(Y\), whether or not it rains for each of 1000 sampled days (Figure 1.7 (a)). The Gamma-Poisson model can help us explore the rate of bald eagle sightings in Ontario, Canada using data on variable \(Y\), the counts of eagles seen in each of 37 one-week observation periods (Figure 1.7 (b)). Finally, the Normal-Normal model can provide insight into the average 3pm temperature in Australia using data on the bell-shaped variable \(Y\), temperatures on a sample of study days (Figure 1.7 (c)).9
1.3.2 Unit 2: Posterior simulation & analysis
When our Bayesian posterior models of \(Y\) become too complicated to mathematically specify, how can we approximate them? And once we’ve either specified or approximated a posterior model, how can we make meaning of and draw formal conclusions from it?
For each fundamental Bayesian model presented in Unit 1, we can use Bayes’ Rule to mathematically specify the corresponding posterior model. Yet as we generalize these Bayesian modeling tools to broader settings, things get real complicated real fast. The result? We might not actually be able to specify our posterior model. Never fear – data analysts are not known to throw up their hands in the face of the unknown. When we can’t know something, we approximate it. In Unit 2 you will learn to use Markov chain Monte Carlo simulation techniques to approximate otherwise out of reach posterior models.
Once we’ve either specified or approximated a posterior model for a given scenario, we must also be able to make meaning of and draw formal conclusions from the results. Posterior analysis is the process of asking “what does this all mean?!” and revolves around three major elements: posterior estimation, hypothesis testing, and prediction.
1.3.3 Unit 3: Bayesian regression & classification
We’re not always interested in the lone behavior of variable \(Y\). Rather, we might want to understand the relationship between \(Y\) and a set of \(p\) potential predictor variables (\(X_1, X_2, \ldots, X_p)\). How do we build a Bayesian model of this relationship? And how do we know whether ours is a good model?
Unit 3 is where things really keep staying fun. Prior to Unit 3, our motivating research questions all focus on a single variable \(Y\). For example, in the Normal-Normal scenario we were interested in exploring \(Y\), 3pm temperatures in Australia. Yet once we have a grip on this response variable \(Y\), we often have follow-up questions: can we model and predict 3pm temperatures based on 9am temperatures (\(X_1\)) and precise location (\(X_2\))? To this end, in Unit 3 we will survey Bayesian modeling tools that conventionally fall into two categories:
- Modeling a quantitative response variable \(Y\) is a regression task.
- Modeling a categorical response variable \(Y\) is a classification task.
Let’s connect these terms with our three examples from Section 1.3.1. First, in the Australian temperature example, our sample data indicates that temperatures tend to be warmer in Wollongong and that the warmer it is at 9am, the warmer it tends to be at 3pm (Figure 1.8 (c)). Since the 3pm temperature response variable is quantitative, modeling this relationship is a regression task. In fact, we can generalize the Unit 1 Normal-Normal model for the behavior in \(Y\) alone to build a Normal regression model of the relationship between \(Y\) and predictors \(X_1\) and \(X_2\). Similarly, we can extend our Unit 1 Gamma-Poisson analysis of the quantitative bird counts (\(Y\)) into a Poisson regression model that describes how these counts have increased over time (\(X_1\)) (Figure 1.8 (b)).
Finally, consider our examination of the categorical variable \(Y\), whether or not it rains tomorrow in Australia: Yes or No. Understanding general rain patterns in \(Y\) is nice. It’s even nicer to be able to predict rain based on today’s weather patterns. For example, in our sample data, rainy days tend to be preceded by higher humidity levels, \(X_1\), than non-rainy days (Figure 1.8 (a)). Since \(Y\) is categorical in this setting, modeling the relationship between rain and humidity is a classification task. We will explore two approaches to classification in Unit 3. The first, logistic regression, is an extension of the Unit 1 Beta-Binomial model. The second, Naive Bayes classification, is a simplified extension of Bayes’ Rule.
1.3.4 Unit 4: Hierarchical Bayesian models
Help! What if the structure of our data violates the assumptions of independence behind the Unit 3 regression and classification models? Specifically, suppose we have multiple observations per random “subject” in our dataset. How do we tweak our Bayesian models to not only acknowledge, but harness, this structure?
The regression and classification models in Unit 3 operate under the assumption of independence. That is, they assume that our data on the response and predictor variables (\(Y,X_1,X_2,\ldots,X_p\)) is a random sample – the observed values for any one subject in the sample are independent of those for any other subject. The structure of independent data is represented by the data table below:
This assumption is often violated in practice. Thus in Unit 4, we’ll greatly expand the flexibility of our Bayesian modeling toolbox by accommodating hierarchical, or grouped data. For example, our data might consist of: a sampled group of recording artists and data \(y\) on multiple individual songs within each artist; or a sampled group of labs and data \(y\) from multiple individual experiments within each lab; or a sampled group of people on whom we make multiple individual observations of data \(y\) over time. This idea is represented by the data table below:
The hierarchical models we’ll explore in Unit 4 will both accommodate and harness this type of grouped data. By appropriately reflecting this data structure in our models, not only do we avoid doing the wrong thing, we can increase our power to detect the underlying trends in the relationships between \(Y\) and \((X_1,X_2,\ldots,X_p)\) that might otherwise be masked if we relied on the modeling techniques in Unit 3 alone.
1.4 Chapter summary
In Chapter 1, you learned how to think like a Bayesian. In the fundamental Bayesian knowledge building process (Figure 1.2):
- We construct our posterior knowledge by balancing information from our data with our prior knowledge;
- As more data comes in, we continue to refine this knowledge as the influence of our original prior fades into the background; thus
- In light of more and more data, two analysts that start out with opposing knowledge will converge on the same posterior knowledge.
In these first exercises, we hope that you make and learn from some mistakes as you incorporate the ideas you learned in this chapter into your way of thinking. Ultimately, we hope that you attain a greater understanding of these ideas than you would have had if you had never made a mistake at all.
Exercise 1.1 (Bayesian Chocolate Milk) In the fourth episode of the sixth season of the television show Parks and Recreation, Deputy Director of the Pawnee Parks and Rec department, Leslie Knope, is being subjected to an inquiry by Pawnee City Council member Jeremy Jamm due to an inappropriate tweet from the official Parks and Rec Twitter account. The following exchange between Jamm and Knope is an example of Bayesian thinking:
JJ: “When this sick depraved tweet first came to light, you said ‘the account was probably hacked by some bored teenager.’ Now you’re saying it is an unfortunate mistake. Why do you keep flip-flopping?”
LK: “Well because I learned new information. When I was four, I thought chocolate milk came from brown cows. And then I ‘flip-flopped’ when I found that there was something called chocolate syrup.”JJ: “I don’t think I’m out of line when I say this scandal makes Benghazi look like Whitewater.”
- Identify possible prior information for Leslie’s chocolate milk story.
- Identify the data that Leslie weighted against that incoming information in her chocolate milk story.
- Identify the updated conclusion from the chocolate milk story.
- Write your own #BayesianTweet. Note that as of May 2020, tweets can have at most 280 characters and emojis count as 2 characters.
- Write your own #FrequentistTweet.
- Write the question from the perspective of someone using frequentist thinking to test the hypothesis.
- Write the question from the perspective of someone using Bayesian thinking to test the hypothesis.
- Knowing that you are qualified, which question would you rather have the answer to: the frequentist or the Bayesian? Be sure to explain your reasoning.
- Identify a topic that you know about (eg: a sport, a school subject, music).
- Identify a hypothesis about this subject.
- How would your current expertise inform your conclusion about this hypothesis?
- Which framework are you employing here, Bayesian or frequentist?
- Why is Bayesian statistics useful?
- What are the similarities in Bayesian and frequentist statistics?
1. a = 1 point, b = 3 points, c = 2 points; 2. a = 1 point, b = 3 points, c = 1 point; 3. a = 3 points, b = 1 point; 4. a = 3 points, b = 1 point.↩︎
Dear readers, if you have experience with frequentist statistics, then you might be skeptical that these methods would produce such a silly conclusion. Yet in the frequentist null hypothesis significance testing framework, the hypothesis being tested in both Zuofu’s and Kavya’s settings is that their success rate exceeds 50%. Since their “10 out of 10” data is the same, the corresponding p-values (\(\approx 0.001\)) and resulting hypothesis test conclusions are also the same.↩︎
There is an extreme exception to this rule. If someone assigns 0 prior weight to a given scenario, then no amount of data will change their mind. We explore this specific situation in Chapter 4.↩︎
http://www.est.ufmg.br/~loschi/labcom.htm, https://www.researchgate.net/publication/339435324_EARLY_BAYESIANS_AND_BAYESIAN_DEVELOPMENTS_IN_INDIA, https://bayesian.org/chapters/(australasian-chapter; south-africa; brazil; chile; east-asia; india)↩︎
These plots use the
weather_australiadata in the
bayesrulespackage. The weather data sets are subset from the
weatherAUSdata in the
rattlepackage. The eagles data was made available by (birdsorg?) and distributed by (birdstudy?).↩︎