Harvard students comment on "Uncertain Risks"

1. Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers?

For non-quantitatively inclined readers, the discussion around risk and uncertainty can be made more illuminating by focusing on relatable scenarios and everyday decision-making contexts. Instead of delving deep into numerical calculations and statistical models, emphasizing the practical implications of uncertainty and how it affects personal decision-making could resonate more with such readers.

2. Suggest another framing of the issues discussed that would be more effective.

Another framing could involve using analogies or anecdotes that simplify complex concepts. For instance, likening uncertainty to navigating through foggy weather, where you can't see what's ahead clearly, but you still have to make decisions based on limited information. This analogy could help convey the essence of uncertainty without delving into technicalities. Additionally, using real-life examples, such as deciding whether to wear a seatbelt while driving or choosing between different healthcare options, could make the discussion more relatable and accessible to a broader audience.

I would assume the most challenging aspect of the article for students unfamiliar with quantitative prediction in general is likely the section discussing the detailed calculations of risk and uncertainty related to COVID-19. Here, the article introduces concepts like fractions, percentages, and the factors affecting these calculations, such as numerator, denominator, and uncertainty range. This involves mathematical reasoning and understanding statistical measures that can be quite complex for those not comfortable in quantitative methods. Understanding how these calculations relate to real-world risk assessment, particularly in a public health context, requires a grasp of both statistical concepts and their application to everyday decisions and policy-making.

The essay's discussion of deer hunting and Russian Roulette is likely the most illuminating component to non-quantitatively inclined readers given that it takes a relatively uncomplicated situation and explains the interplay between risk and uncertainty in either scenario. Each example serves to emphasize the importance of estimating odds in related situations, more common in daily life. Furthermore, the comments made by Dan Gilbert regarding humans' outlook on likelihood further contextualize the differences between risk and uncertainty as well as its manifestations in subsequent human action.

The parts of the essay that give actual, quantitative examples about each scenario would likely be the most helpful for non-quantitatively-inclined readers. Simply telling someone what the difference between risk and uncertainty is not very useful in a vacuum, but having one control "variable" (i.e., the level of risk) helps to illustrate it greatly. That is to say, showing how the moon landing has high risk because the astronaughts might die in any number of ways, and how a rare disease would have low risk if it doesn't kill often, but both have high uncertainty due to the large unknowns of a moon landing or the disease's erratic behavior, provides extremely helpful contrast for demonstrating the difference between the terms.

The part of the discussion that was most illuminating for me (a non-quantitatively-inclined reader) was the clear way uncertainty and risk - and the differences between them - were explained. Using examples like the Apollo missions (high risk AND high uncertainty) and russian roulette (high risk, low uncertainty) really put things into perspective. Therefore, the high uncertainty nature of the COVID-19 projections became much more understandable in this framework.

Hello,

I found this reading to be thought-provoking. Many people in society will spew random statistics they saw or heard on the news without considering the factors that may influence this data. This is what non-quantitative thinkers will look at; due to this, they do not grasp the true complexity of the situation and what it takes to resolve such an event. For the Russian roulette analogy, many people may think, "Yes, there is a 17% chance of a round being fired, causing almost certain death," but those are not the true odds. I say this because the essay mentioned that revolvers misfire or malfunction, which can affect 1 in 6 chances of a live round going off. This is uncertainty because we are, in a sense, in the dark about the millions of factors that could have affected the weapon, person's hand movement, etc., which means without this information, we will never know the true odds of death in any given Russian roulette round. 

Best,

Joey Cano

For non-quantitatively-inclined readers, I would expect the introduction to be the most illuminating. The analogy to Russian Roulette and the deer hunting were helpful for setting up the conversation on risk and uncertainty. By bringing up this example throughout the post, the article provides a relatable way to understand these complex concepts. Using the Apollo 11 voyage as an example for high-risk and high-uncertainty was another helpful analogy made for non-quantitatively-inclined readers. Using COVID-19 was also another good example that grounded the reader in the concepts of data and uncertainty.

I think that the discussion on the uncertainty around the risk of COVID would be very helpful for non-quantitatively-inclined readers. When you are estimating something like risk, you have an average estimate of the risk and some degree of uncertainty around your estimate. This article’s discussion on COVID risk does a great job of illustrating how with few data points, we can still find an estimate of risk but we will have a larger uncertainty. This is because we did not know enough about the virus (early in the pandemic) to be certain if our best estimate of risk is close to the true value. As we see more cases of COVID, we can say with more certainty what the actual risk of the virus is since it is far less likely that we are just seeing a non-reflective sample of how dangerous COVID is. Knowing this concept is important in statistics since all estimates have some degree of uncertainty and understanding that uncertainty can help a lot in interpreting the statistical values we see in the world.

Readers who are not as familiar with quantitative concepts probably would find the section distinguishing "risk" and "uncertainty" especially helpful and insightful. This part of the discussion demonstrates how risks can be quantified, whereas uncertainties often cannot. Using accessible scenarios, such as playing Russian Roulette versus encountering unpredictable challenges in hunting, would help readers clarify the implications of each in real-world decision-making. Understanding this distinction can profoundly impact how individuals and organizations prepare for future challenges, emphasizing the importance of flexibility and resilience when handling unknowns.

The parts of the discussion that I expect would be the most illuminating for non-quantitatively inclined readers would be the introduction with analogies and the description of human perception of risk. The initial introduction, comparing the certainty of outcomes in hunting and Russian Roulette, can help non-quantitative readers grasp the fundamental differences between risk and uncertainty because analogies provide a relatable way to understand complex concepts. The section discussing how humans typically perceive risk, categorizing it into "it will happen," "it won't happen," and "it might happen," can resonate with non-quantitative readers because it calls awareness to something that they do without thinking.

In order to better understand a high-risk high reward situation, you can change the framing to one of a roller coaster simulation. Imagine A new type­ of roller coaster gets introduce­d at an amusement park. It promises safe­ty and excitement. Be­ing the first of its kind, predicting safety is difficult. Ride­s can break down sometimes. Without past e­xamples of this specific ride, calculating risks prove­s challenging. This lack of information makes trying the ne­w ride uncertain regarding pote­ntial risk. Trying a new rollercoaster and being an early reviewer can be fun, but it may also come with alot of dangers

As a non-quantitatively inclined reader, I found the example from The Deer Hunter to be the most illuminating. Although I had no prior knowledge of the movie, these simple and easy-to-understand examples helped me comprehend the difference between risk and uncertainty without a high understanding of statistics or data science. Simple examples like this make the essay more accessible to a broader audience that does not need to be an expert in the topic to understand the message the writer is trying to send.

1. I believe the parts that were most illuminating for non-quantitatively-inclined readers was the comparison between the low-risk low-uncertainty situations in contrast to the high-risk in general (both high uncertainty and low). But, for this example, let's keep it at high-risk low-uncertainty. People are generally good at making judgements on things that are obvious, so, when presented with the option of lying on a couch, versus participating in a round of Russian Roulette, then people would be highly inclined to take the couch option, even though they know the uncertainty of Russian Roulette. Even without knowing exactly the "odds" as the essay mentioned, non-quantitatively-inclined people will be good at measuring risk without the need to pay too much detail for uncertainty.

1. As a non-quantitatively inclined person myself, I think the use of the film The Deer Hunter was a good rhetorical device for breaking down the difference between uncertainty and risk and then walking through what different combinations of these concepts entail. For example, it made a lot of sense to me that the Russian Roulette game had a high amount of risk, but a low amount of uncertainty. The risk refers to the chance of dying while playing the game, while the uncertainty refers to the chance of the risk calculation being off. Because of how close of a shot the Russian Roulette game provides, it is unlikely that there will be much uncertainty. Continuing with this line of thinking, the hunting of a deer could be viewed as a high risk, high uncertainty situation. If we assume the hunter is pretty good and only takes shots that they have a good chance of hitting, as from my understanding most hunters do, then we say there is a high risk. But, similar to the Apollo example, there is high amount of uncertainty, over a longer distance with more variables, it becomes much more challenging to evaluate the exact risk.

The essay on ‘uncertain risks’ provides qualitative and quantitative insight on risk vs uncertainty. Here, risk is defined as the fraction of number of people affected divided by the people that could have been affected. The part of the essay that would be most illuminating for non-quantitatively-inclined readers is the discussion about uncertainty in the case of russian roulette. Here, uncertainty is illustrated through a very logical way, as a percentage of times a gun would not work or a person would not die. This is a non-quantitative explanation of a usually quantitative-heavy subject of uncertainty.

Another framing that might be more effective in its own way is diving deeper into how figures for uncertainty are arrived at, how the uncertainty of 370k people is arrived at. Is it constructed based on previous similar pandemics or current, ongoing data?

1. For non-quantitatively-inclined readers, the discussion of uncertainty in the real-world examples of Russian roulette and deer hunting might be most illuminating. Readers who have little or no background in statistics or data science will already be looking to connect the ideas in the article with something they are familiar with. If those familiar everyday examples are already available to break down the subject in simpler terms, then the whole learning process is expedited. 2. Similar to my discussion in the first question, I think another effective framing of the issues would be sharing the specific stories of individuals and their outcomes when dealing with this issue. These stories would provide more real-world examples for the readers as well as an inside look at the numbers to see how the outcome of one person contributes to the overall data. Every additional small part of the discussion that the readers can comprehend exponentially increases their understanding of the data and the uncertainty if they are non-quantitatively-inclined.

To answer Prompt 1—"Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers?"—I would say that the example connection to Deer Hunter / Russian Roulette would be the most illuminating for non-quantitatively-inclined readers. Giving these real-life examples make the discussion super clear since most people are familiar with the nature of these games, and they also take the spectrum of risk/uncertainty to the absolute extremes. For example, in Russian Roulette, it's super easy to see how it is very high risk, since the prospective outcomes are either life or death, and the uncertainty is also clear to see since the nature of the game is based on the 1 in 6 outcome of a revolver chamber. Thus, these are great examples that underscore the difference between risk and uncertainty in that context.

For non-quantitatively-inclined readers, I believe the most illuminating discussion was the consideration of fractions, rather than a raw integer, as important in assessing risk. It might be scary to hear, for example, that 100,000 people died of X cause. However, if 1,000,000,000 people are engaging in such an activity, then the fear can be mitigated by the proportion. Adding uncertainty to that estimate, which grants an upper and lower bound to that risk, can also change how a person feels about it. One could be more risk averse, making them inclined to believe the upperbound, and another could be a risk taker, making them okay with the lower bound.

I really appreciated the deer hunt and Russian Roulette analogy to demonstrate uncertainty. I think it uses two situations that are very simple and very easy to understand. Everyone is familiar with the game Russian Roulette -- in fact, everyone is familiar with the certain risk associated with the game. Therefore, comparing this to a deer hunt where it is common to use multiple shots to take down a deer or to miss altogether is a good comparison. I think I would have appreciated a little more imagery on the deer example to hammer the point home, however.

As a non-quantitatively-inclined reader, I found the analogies comparing high/low-risk situations, like Russian Roulette and lying on a couch, to be the most illuminating part of the discussion. These examples effectively explained the concepts of risk and uncertainty without much math. The Russian Roulette analogy illustrates a low-uncertainty, high-risk situation, while the couch example represents a low-risk, low-uncertainty scenario. The essay also explains that humans typically group likelihood into three categories: "it will happen," "it won't happen," and "it might happen," with the word "might" capturing everything between "will" and "won't." This categorization helps non-quantitatively inclined understand how people often think about risk in everyday life, without attaching specific numerical odds. By using this kind of categorization and analogies, the essay was able to connect technical risk assessment and intuitive understanding. 

I found the framing examples in this article very useful for understanding and compartmentalizing the different kinds of risk and uncertainty. Considering the risk and uncertainty in Russian roulette compared to the risk and uncertainty in hunting makes these concepts more accessible for non-quantitatively-inclined readers and allows them to grapple with these complex topics in familiar contexts. By contrasting the risk and uncertainty in these two scenarios, the difference between them is made obvious. The only thing I think could potentially make this framework stronger is a visual like the one we looked at in section, with a scale from low to high risk and a scale from low to high uncertainty on the x & y axis. Positioning Russian roulette and hunting on this visual might further clarify these concepts and help to strengthen the explanation overall.

Suggest another framing of the issues discussed that would be more effective.

Another framing of the issues discussed could focus on storytelling and real-life scenarios to engage readers emotionally while conveying the significance of risk and uncertainty. Instead of diving straight into numerical examples, the piece could begin by narrating relatable scenarios where individuals are faced with decisions involving risk and uncertainty. These narratives could then transition into discussions about the importance of understanding risk in decision-making, drawing parallels to everyday experiences. By using storytelling as a framing device, the piece could capture the attention of readers who may not be naturally inclined towards quantitative analysis and provide them with a more accessible entry point into the topic.

I guess I did a little bit of a combination of the assignment by touching upon both questions. For one, I think the most illuminating part of the discussion for non-quantitatively-inclined readers would likely be the discussion how humans perceive risk and uncertainty in various scenarios. The article touched upon examples like Russian roulette and the Apollo mission with regards to risk and uncertainty, which were extremely relatable and made the article a lot more personal. This leads into my answer for the second question, which is to potentially continue to expand on some cool ideas of the evaluation of risk and uncertainty. I think some small, funny examples that most humans interact with on a regular basis could also help a non-quantitatively-inclined learner to understand/think about the material. One quick example that I could think of is lottery tickets/gambling in general. Examples that are even more personable can help students learn about the core concepts of uncertainty and risk that we talked about extensively in class.

Most illuminating for non-quantitatively-inclined readers:

The most illuminating part of the discussion for non-quantitatively-inclined readers is when Professors Goodman and De Vivo illustrate risk and uncertainty through observable examples. By translating statistical numbers into comparisons of real-life scenarios, readers can visualize the concepts in a tangible way. This approach helps demystify the quantitative analysis by using everyday language to describe what risk and uncertainty look like in practical terms, thus making the abstract more accessible. This method is especially effective when discussing complex topics like COVID-19, where understanding the nuances of risk can impact public perception and behavior.

Another framing of the issues discussed that would be more effective:

An alternative framing that might enhance the discussion's effectiveness is to broaden the focus beyond COVID-19 to include a variety of risk-related scenarios. By comparing and contrasting these scenarios, the article could explore how different levels of uncertainty affect decision-making across diverse situations. This approach would highlight the universal importance of understanding risk and uncertainty, not just in the context of pandemics but in everyday decisions as well. Additionally, incorporating insights into how the public perceives and reacts to statistical data could deepen the conversation about the interplay between information, emotion, and action in the face of uncertainty. This could provide a richer understanding of why people react differently to similar risks and how this shapes public policy and personal choices.

Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers?

I think the discussion on what uncertainty means was the most illuminating. The analogy and comparisons between lying on a couch and Russian Roulette helped make the "risks" of uncertainty very clear; we can be highly certain, but that doesn't tell us too much about the risk of the result. Thus, in order to better express predictions, it's critical that different scenarios are given when summarizing results. Although the best method would to directly display a distribution, this is obviously difficult because it probably carries too much data. Thus, including something like the 2.5%, median, and 97.5% result is probably succinct yet expressive enough.

Suggest another framing of the issues discussed that would be more effective.

A good reframing of these issues could focus on how the public interprets data and information given to them. This could be discussing the challenges in communicating uncertainty effectively, especially in a context where information is constantly evolving (like in the COVID case). By highlighting the uncertainty in presenting data, acknowledging uncertainties, and explaining the process of scientific inquiry, the public can gain a deeper understanding of information and make better informed decisions.

The essay by Professors Goodman and DeVivo was filled with enlightening analogies that are very useful for readers who are not quantitatively inclined. In particular, the analogy having to do with the Apollo 11 voyage and “extremely high-uncertainty experiences.” This was especially insightful because the pandemic itself was an experience that was full of high -uncertainty and possibly one of the most uncertain times most people had experienced. In this case it was important to distinguish the risk from the uncertainty — something that people were not necessarily doing during the times of the pandemic. The thorough description of uncertainty because of lack of information was important to parallel with the lack of data from COVID-19 outcomes. On the other hand, the high risk factor was an additional factor for Apollo 11 whereas it was not necessarily the issue for COVID-19. Goodman and DeVivo debunked this worry that the issue with COVID-19 was risk of death in comparison with other threatening illnesses like the Spanish flu. This discussion brings to light the fact that COVID-19 was not like Apollo 11 and that panic confused uncertainty with risk. 

Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers?

I found that the Russian Roulette story and the laying on the couch example are the two parts of the discussion that would be most illuminating for non-quantitatively-inclined readers. The two examples display the difference between uncertainty and risk. The uncertainty of risk in Russian Roulette is very low or zero and similarly the uncertainty of risk when lying on the couch is also very low, but the actually risks of death when engaging in the activities are very different. Russian Roulette obviously has a high risk of death while laying on the couch has a low risk of death.

1. Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers? I expect the discussion on how people often misunderstand the terms "risk' and "uncertainty" to be the most illuminating for non-quantitatively inclined readers. We use general terms like "safe" and "risky" daily, therefore adding specific numerical values to these words can be challenging and a new concept.

   
   

2. Suggest another framing of the issues discussed that would be more effective. Another framing could be linking these concepts to familiar, everyday scenarios. For instance, financial planning, health, or even routine choices like commuting. This could help brideg the gap between abstact statistical concepts and practical ones.

   
   

For non-quantitatively inclined readers, I believe that the discussion that focuses on the emotional aspects of risk and uncertainty would likely be most illuminating. An example is the exploration of why people fear certain risks more than others, even when the actual likelihood of occurrence may be low. By delving into the psychological and emotional factors that influence our perception of risk, people can better understand their own reactions and decision-making processes. Additionally, highlighting real-world examples, like the comparison between COVID-19, the Spanish flu, and Russian Roulette, can help people grasp the relative risks and uncertainties involved in different situations.

1. For non quantitatively inclined readers, I believe the portion with the Russian Roulette and couch analogy is quite intriguing because it displays two scenarios that are on two ends of the spectrum. The Russian Roulette describes a high risk situation, whereas the lazy couch scenario describes a low risk, and low uncertainty situation. It makes a clear distinction between two cases.

Suggest another framing of the issues discussed that would be more effective.

I think the issues talked about in this essay were presented in a fairly digestible way, such that it would be quite straightforward for people who aren't super well-versed in prediction and risk assessment to follow along with relative ease. I think that another way this essay could've been framed to make it more effective would've been to introduce each different topic in a narrative/more personal format. Personally, I find it easier to understand a lot of concepts when they're framed in a personal way that I could potentially find relatability in. Of course, a lot of the topics presented were relatable and every day -- lying on the couch and dealing with COVID-19, for example -- but I think adding another element of personality, for example describing each topic as if it were a story following one person or a group of people, or maybe the authors describing the topics through more personal lenses, could've made the discussion of these issues a little more effective.

1. Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers? I feel that the Russian roulette and hunting examples in the introduction are incredibly effective at explaining risk for non-quantitatively-inclined readers, such as myself. I appreciated the visuals to make more abstract concepts involving risk and uncertainty more tangible. The hunting/life parallel was also particularly illuminating; in other words, life has a tendency of throwing curveballs when you least expect it, just as a hunter is likely to experience unpredictable conditions in action.

Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers? I think non-quantitatively inclined readers will benefit most from the attribution of the words “risk” and “uncertainty” to phrases such as “odds” and “how sure we are about those odds” respectively. Without ever calculating the risk or uncertainty of an event, a non-quantitative reader will be able to understand that an example of risk is just the fraction of people who died having contracted a disease and uncertainty is how certain we are about that fraction.

Which part(s) of the discussion do you expect would be most illuminating for non-quantitatively-inclined readers? I think that the discussion of risk and its relationship to extremely high uncertainty was quite interesting! I thought that the example of the Apollo voyage provided a really world-relevant (and thus interesting) example of risk in the case of high uncertainty experiences, and would help explain the concepts and relationship well. Specifically, I love the proof-like structure where the givens (factors that cause high risk of death) then lead into the conclusions. I also love how this seamlessly leads into the discussion of COVID-19 and connects the examples well! I think that a similar event to the Apollo example is the risk of dying in a self-driving car - the risk of death is high if the car makes a mistake (since car crashes are often fatal) and the uncertainty of whether it will is also high (since we haven't tested them very much).

I think discussing risk in the context of the Covid pandemic provides an interesting opportunity, given how much the perception of risk affected life during the pandemic. I believe that describing risk in terms of the question: "what percentage of people afflicted with this condition have died" is really enlightening. Upon hearing that a certain disease has a percentage rate of p, I think that humans / the general public are inclined to believe that their own chances of survival may essentially be equated to flipping a coin with probability p of heads. However, framing it in terms of the most general definition sets the stage for a more in-depth understanding of the reality of the disease and also allows for further understanding of more advanced topics, such as conditional probability.

I thought the real-world scenarios that Dr. Goodman utilized to explain low-uncertainty experiences were excellent choices to appeal to a non-quantitative audience. The Russian Roulette example vividly demonstrates a situation in which there is a high risk of death and low uncertainty. A reader that is unfamiliar with statistics can still easily understand the certainty embedded within a system where the bullet will kill with only "one shot."

Conversely, the "lying on a couch" example showcases an everyday activity with an extremely low risk of death, but high certainty as well. This pair of scenarios demonstrates quantitative, statistical truths, in a way that translates the abstract concept of uncertainty into concrete examples.

I think the inclusion of the Deer Hunter and Russian Roulette analogies throughout the article prove to be helpful to non-quantitatively inclined readers. As is addressed early in the article, misunderstanding or confusing the words "risk" and "uncertainty" can be dangerous, and doing so is a key reason why many misunderstand COVID-19. I think the first paragraph of the article does a great job of defining these words through the easily-understood analogies of hunting a deer and Russian Roulette. The article addresses all of the variable factors involved in determining the odds of killing a deer in a single shot which perfectly defines uncertainty, and the blunt 1 in 6 chance of dying in Russian Roulette which defines the other extreme, nearly complete certainty. These examples clearly differentiate risk, the chance of death, from uncertainty, how sure we are about the amount of risk, which I think is really helpful for non-quantitatively inclined readers.

As someone who wasn't entirely sure of the difference between risk and uncertainty, I found the example of the gun and Russian roulette extremely enlightening in differentiating the uncertainty of death -- which is almost 0 in the case of a bullet entering the brain -- and risk, which is the probability that the bullet will be in the chamber fired; in this case, 1/6. I also appreciated the examples of other easily visualizable isntances of different uncertainty/risk tradeoffs, such as the living room couch for low risk/low uncertainty and space exporation (Apollo 11) for high risk and high uncertainty. These examples really helped flush out the concepts before they were applied to the low risk, high uncertainty scenario of COVID-19

I think for non-qualitatively inclined readers, the covid risk discussion is the most useful. Particularly, when it says that the average deaths from the United States are only estimated to increase by 10%. I think that most people think that a relatively low number of people die each year, and so just one more person dying per 10 estimated deaths in a normal year would not concern them greatly. As such, I think they would be able to understand the chance of dying from Covid is not necessarily that great, and they can view the risk with more of a probability assessment rather than a "might happen" assessment.

I found this essay very engaging and provided the reader with commentary and discussion that could be more easily digested than trying to interpret lots of statistics and quantitative evidence about this subject. Personally, I would consider myself a non-quantitatively-inclined reader and find that my best understanding of topics comes from broken down discussion and interpretation that is focused on the topic rather than trying to interpret all of the numbers that support that topic. In this essay, I appreciated the lens that deconstructed the complexities of these ideas; even in the first paragraph, there was a quote from a famous movie that one might know and then some explanation that was connected to common known activities like hunting and Russian roulette. From that point, I already had a better grasp of the knowledge trying to be conveyed than if I had tried to read a daunting quantitatively focused essay. The fact that the Deer Hunter Russian Roulette story returned to the conversation to describe the differences in uncertainty and risk which was also very illuminating and grounded my understanding. The reader is then able to connect the examples at the beginning of the essay to how it all relates to COVID-19 and epidemiology, which was a great way to help the reader to best understand the basics of risk and uncertainty and then more easily apply it to their uptake of this knowledge surrounding the current pandemic.

I believe the main takeaway of the essay for non-quantitatively-inclined readers should be the discussion of why consider uncertainty in the first place. Humans are not perfect and when we attempt to theorize, experiment, or evaluate observable phenomena there is no way our results will hold 100% of the time. However, having a notion of 'error-spread' or uncertainty is crucial to make this idea transparent and thus make better science and even everyday decisions.

The section that I expect would be most illuminating for non-quantitatively inclined readers would be the part two situations with different uncertainties. In this section it looks first at a low-uncertainty situation (Russian Roulette) then at a high uncertainty situation (Apollo 11). I think it is very important to apply these easily comprehensible examples towards two drastically levels of uncertainty because it makes it a much easier access point. Jumping straight into numbers with little context can lead to people just giving up and tuning out the rest of the information. However, using these tangible situations and very little, simple math it is much easier to get a foot in the door of this information. Once that ground layer of understanding people can feel calmer and more confident that they will be able to comprehend the rest of the information. Also, having two different examples with different levels and factors of uncertainty allows the coinciding information about COVID-19 much easier to comprehend.

Unequivocally, the passage in this essay on COVID statistics was eye-opening to read for someone "non-quantitatively-inclined" such as me. While the language of low-uncertainty and high-uncertainty experiences was highly interesting and illuminating, I can't imagine organically changing behaviour based on these discoveries. When the statistics are set out in such a way that they are in the essay though; in plain language, and particularly in comparative context; to the average annual death rate, to the death rate of the Spanish flu, etc., I feel like I'm being introduced to a coherent narrative, based in fact, which will tangibly impact my thoughts, opinions, and thus behaviours.

The content, then, I think is excellent! It sounds silly, but perhaps a good way to impart it other than in essay format would be through a TikTok. While a short, tiktok format videos starts off mostly being watched by the younger demographic that likes the app, I see the videos being shared now frequently on other platforms, such as Twitter and Facebook, which have a larger reach for broader swathes of the population!

I surely agree, @vincentli -- these are the draft images we ultimately didn't include, but clearly should!

Similarly to what Will has written, I think that the part of the article most illuminating for non-quantitatively inclined readers would be a very number-heavy paragraph. In the paragraph that begins with "What fraction of people....", Prof Goodman and Prof De Vivo clearly spell out the calculations for what the fraction of people having the experience being evaluated die from COVID-19 is. Because this solution is prefaced with step-by-step arithmetic, this part of the discussion will be most illuminating for non-quantitatively-inclined readers such as myself.

I think the most illuminating part of the discussion for non-quantitatively-inclined readers is the part discussing the difference between low-uncertainty experiences and extremely high-uncertainty experiences. This part illustrates a good idea of what uncertainty is without using numerical data. Furthermore, the comparison between hunting and russian roulette prior gives a good explanation of the difference between risk and uncertainty.

As a non-quantitatively inclined reader myself, there are a couple of aspects of this impactful article that most clearly break down the reality of risk and uncertainty as it relates to the coronavirus pandemic. First, the distinction between the four high/low-risk and high/low-uncertainty categories earlier on in the article is helpful. Even if the reader is fuzzier on the exact numbers, they can easily conceptualize the relative risk and uncertainty of the common sense examples for each category. Following this discussion with the implication that the pandemic lies closer to space missions on the uncertainty spectrum, but closer to lying on the couch on the risk spectrum, makes the argument easy to follow. An additional strength is framing data as "prior observations." Like the previous example, this part of the discussion relates the quantitative to something non-quantitative that is perhaps recognizable to a wider audience. Finally, the explanation of uncertainty as "how well we know the odds" is a concise but effective way to drive home a key point from this class, that just because there is uncertainty does not mean that scientists don't know what is going on.

After reflecting on “Uncertain Risks” by Profs. Goodman and De Vivo, I found the paragraph that compares the COVID-19 pandemic to the Spanish flu most illuminating as a non-quantitatively-inclined reader. Readers who are not quantitative-focused may be more willing to listen to articles and reports that compare COVID-19 to the Spanish flu. However, the authors explain in this paragraph that there is a 100x lower chance of dying from COVID-19 than dying from the Spanish flu. It is easier to visualize how much lower the risk with COVID-19 is when explicitly compared to the Spanish flu. The quantitative points in this paragraph also clearly show why the two events are not necessarily comparable without focusing on specifics that would confuse someone who is not quantitatively inclined. This paragraph is also illuminating because many media articles compared COVID-19 to the Spanish flu when in reality this comparison is not as strong as initially presented.

I think that the three paragraphs starting with "Looking at current projections for March 1, 2021--likely just before widespread vaccine distribution--" and ending with "and people misunderstand uncertainty" are the most illuminating paragraphs for non-quantitatively inclined people. This passage breaks down raw pandemic data and contextualizes it using jargon-free language and historical examples that most lay readers could understand. One of the biggest sources of misleading information were sensational news articles that provided raw statistics and "what-if" scenarios without contextualizing the data in terms of certainty, historical examples, or warranted level of concern. This article demonstrates that one can explain risk and uncertainty to lay readers in a digestible format that fully acknowledges the danger of the pandemic without causing undue anxiety about possible but exceedingly unlikely scenarios.

I think the most interesting aspect of our discussion would be the general differences between risk and uncertainty, as discussed in the essay. As someone who isn't quantitatively inclined (despite the valiant efforts of this course), the essay did an excellent job in highlighting the distinction between these two concepts in a way that can be followed by those who struggle with mathematics. I think what will also be interesting is discussing how these concepts relate to decision making. If you have a low risk but high uncertainty situation, your choice of actions can go in a divvy of directions. While prediction to decision seems to be the most straightforward way of seeing things, I think risk and uncertainty play into our decision making in a way that is more stressful than predictions.