Before beginning the lectures for today, please fill out one of the surveys below.

If your last name begins with letters A-L, take survey 1.

If your last name begins with letters M-Z, take survey 2.

**Remember to record your response to the survey in your private notes. You will refer back to this response later during the lecture.**

Then, watch **both parts** of the lecture by clicking here, or go to Blackboard, then navigate to: Content–Guest Lectures.

This link will take you to the Blackboard page hosting the article Prof. Bayaz-Ozturk mentioned in her lecture, “Closing in on Cancer.”

After watching both parts, please make two posts below as comments on this post, or as a response to another student’s comments. These comments are due before the following class period (i.e. before 2:30pm on Thursday 4/8/21) and will only count towards your grade if made before that time.

The first one is your own individual thoughts and response to the lecture. This post should be at least 75 words long. You should interact with main ideas, although what you say is up to you. You may want to criticize an idea, agree with something, or offer further insight or thoughts about some topic discussed by our guest. Or, you may want to pose a question for others about the lecture. I expect the post to interact with the content of the actual lecture, and not just the day’s topic or other thoughts you may be having.

The second post is an interactive comment, posted as a response to another student’s comment. There is no length requirement on this post.

This lecture was very informative and thought provoking. It highlighted ways in which people can intentionally and unintentionally misconstrue information. It explained the ways that people manipulate statistics for their own interest, as well as how people may draw conclusions without fully understanding the statistics. Learning about the Bayes’ Rule was helpful but realizing that doctors and scientist do not always use this rule to ensure that they understand the statistics, is very concerning. As stated in the lecture this misinformation can affect lives, outcomes and have consequences that affect our health. It is not wise to use different metric systems, with different variables and try to compare them to argue your case. It has definitely opened my eyes to not take all statistical information at face value, to consider additional factors and critically think about the information provided or not provided. That statistics can be worded and compared in ways that are not always for the right intentions.

Not taking statical information at face value is so important. Its is so important to critically think about where the information is coming from, how accurate it is, or the form at which the data was conducted, or if there is any bias, because all that could lead one to believe information and data that is wrong even though its correct or for the average person who may not even have time to research themselves might think the information is correct whereas its wrong and has errors

Hey, I feel the same way based on your response to the guest lecture. We often obtain a lot of information. Most or majority of the information is false or not a proven fact.

Hi Adela,

I agree with your post regarding this lecture. Too often data is presented to individuals by those who may not fully grasp the topic or may only take bits and pieces of a study/statistics that sounds appealing to them. In the world of medicine in particular this can have a profound impact on patient’s lives. False positives can lead to treatments that were unnecessary whereas false negatives can lead to lack of intervention in cases where time is of the essence. While there is no way to avoid human error, this lecture stressed the importance of using statistics to help deliver more accurate information to the patient and medical practitioners in order to minimize the rate of error.

I agree with Adela about how people can unintentionally misconstrue information because most doctors want the best for everyone. however, if you were taught the wrong information it may not be your fault but you still need to give yourself a chance to go and become knowledgeable with your own information and studies.

This lecture was very informative about outcome. It illustrated that we have probability in our life and we may not recognize it that there is one. It provided information and statistics about an event probability which is when you do something there is a chance that an outcome can be produce and they even show the formula on how to calculate probability. The total probability which is calculating the total probability and bay’s rule helps you figure out a conditional probability. By using the probability formula we can calculate the probability of women testing positive and the percentage of having breast cancer. Information cannot always be right because people can manipulate it to their own benefits even though it is presented the right way and can be made to be false information. Data was provided about people who are diagnosed with a symptom and the survival rate. When it comes to statistic data we can’t always rely on others and we should educate ourselves.

Hello, Neil. I agree with you that the information we get is not always correct, so we need to make judgments when we analyzing information. Checking the reliability of the information we have is essential. Rather than blindly trust the information provided by others, We should use our knowledge to perform analyses and come up with our conclusions. Also, it is significant to identify where the data comes from and how the information is processed.

The guest lecture had a lot of information to give about probability which is a more mathematical view on case studies made in the health field. found it interesting that there is something called statistical illiteracy which the guest said is common among patients, doctors, politicians and journalists. you see it every time when people throw numbers out about the probability of something occurring or happening without concrete proof of it which i agree could be cause serious health consequences because for example just hearing something like 10 out of 1000 women would have breast cancer could be ignored by some and taken serious by others.

I agree that when people don’t tell the truth when it comes to a statistical data it can have a lot of consequence especially when its health related.

I agree with joseph. Probabilities can’t always be reliable. It is important to carefully analyze the statistics before coming up with a conclusion.

Hi Joseph,

Awesome catch. I agree, after hearing this lecture I am so skeptical at numbers that medical professionals throws at their patients. These numbers that the patients hear is so important because it may be a factor as to whether or not they will choose to or procced forward with a treatment or surgery. But, not its scary because how much of these numbers that are being told are actually true and if it is true what factors did you use to come up with it.

The lecture to me stresses the importance of looking at the data critically yourself. “Data” presented by non-statisticians may be taken out of context or more importantly not fully understood. One must always consider the scientific method and how well a study was conducted, who conducted the study and who funded the study. This leads to the validity and importance of peer-reviewed journal articles. They provide an additional level of confirmation by other professionals in the field in which it hopes to decrease the rate of misinformation. This does not negate the fact that others may take bits and pieces of information and misconstrue them to fit whatever narrative they are after. Whether this is done intentionally or unintentionally still has consequences that can and will have profound impacts on individuals lives. At face value 10 out of 1000 patients might not be bad in terms of statistics, however if you are one of those 10, your outlook on the situation is not great.

Hello Agnieszka, it is very true what you pointed out. It is very important to consider the sources behind the statistics, the scientific method, and who carried out the study since incorrectly displayed and misinterpreting information can lead many people to make wrong decisions that can affect them.

Thank you for your response, it is important to stress how not having all the data from a case can manipulate a situation. Often times people are misinformed and the use of their own probability can help clarify confusion too give them the most accurate statistics when making decisions.

hey Agnieszka I agree with your post , stats can be misleading and can paint the wrong picture, it can also give a false sense of security especially in the health field. its very important to look and interpret the data yourself to prevent biases or to to be better informed. this will come handy when its time to make important decisions for yourself or family.

This lecture is very informative and teaches us about how probability theory could apply to our daily life. Professor provides a review of probability theory and gives examples of how probability theory could affect decisions making process. The way information is presented could affect the way people interpret it and lead to misunderstanding. As the survey provided before the lecture, it could lead to different results when giving the information in different ways. When providing the data in natural frequencies, it is easier for us to find the correct answers. Also, I learned that when we look at statistics, we should look at the information behind the number instead of just read the number. Everyone has their way to interpret data, so it is essential to analyze the statistics by ourselves before we make conclusions.

I agree when you say that we need to look at the information behind the number. It can create a misunderstanding/confusion.

This lecture explains the probability of an outcome and an example of how it’s determined based on flipping a coin and the chances of that coin landing on heads or tails. The probability of this outcome is always 50%. The professor explained it very thoroughly and with details which helped me understand the concept of probability further. I liked that the professor made a diagram in the second lecture to explain the probability of breast cancer. This made it easier to understand. From the second lecture, it seemed like probabilities aren’t always accurate, there are exceptions. For instance, how Americans have a higher survival rate for prostate cancer than England. This is due to regular prostate exams that are available in America but not in England therefore it’s harder to catch cancer on time.

Hi Reshma, thanks for your contribution. Please note that the probabilities presented in the article or doctor’s report may be correct, however, we need to be careful when interpreting these numbers or their relevance in a particular context. For example, Gigerenzer points out that comparing cancer survival rates between countries is not appropriate because of the possible differences in cancer screenings.

I am not going to say that I am shocked with the idea that there is manipulation with statistics in order to benefit a higher power. Throughout the entire pandemic we faced something similar where countries and states have lied about COVID cases in order to rush the process of opening their areas. A certain ex president of the United States might call this “fake news”. This is why we need to fact check when we hear certain statistics. When we hear a statistic, we do not get a source. We only receive what others want us to hear.

Hi Tony !

You hit the nail on the head with this response. We have been bombarded with Covid statistics and not all of them are being used correctly. We believe so many statistical reports and they influence the decisions we make on a daily bases. The CDC has also given us statistics that they themselves has to retract at times.

Hi Tony! I agree with you in the manipulation with statistics to benefit the higher power and especially now with COVID. I feel like there’s more manipulation now on the news because COVID is fairly new, with new research made daily. By it being new, people can be manipulating it in different ways and makes it harder to find reliable evidence in certain platforms, at the same time there is so much actual evidence but some people refuse to believe it.

Hello Tony,

I think you used a great example to show the cons of using probability in the medical field. It does not really give an exact calculation. For the longest, I feel like a lot of patients were at first passing away from other respiratory issues somehow doctors were relating it to covid regardless of what it was. I am sure within the panic when the pandemic first started a lot of false cases were reported.

I found this lecture to be very informative and it is also makes you stop and think critically and not just take numbers at face value. It shows the importance of doing your own research when presented with statistical values, more often we are presented with numbers to a condition but we do not know who did the study. I like the example of Giuliani and how his statement about his survival rate can be interpreted in many different ways.

I agree with you. We do need to do our own research to find out what’s behind those statistics. It is especially true in the medical field because most statistics in medical field are from experiments and surveys. There are so many variations that could change the result.

You mentioned experimental methods used in research. That is a really good point. We need to know the assumptions that are made and whether similar results can be reached when we change those assumptions. Good point!

The reading was very informative and interesting, I was able to understand that this science develops links, mathematical relationships, and operators of increasing degrees of complexity that somehow help us understand the reality and nature of problems and guide us to make decisions to solve them. However, it is also true that statistical data, incorrectly interpreted, can lead us to make wrong decisions and even influence our way of thinking. That is why it is key to carefully analyze the numbers and investigate the sources behind them and not just completely rely on the figures people show us.

Hi Meilly, i agree with you that incorrect information from statistical data can cause a person to make a wrong decision because they are going by numbers that they are giving and not doing much research on their own

I must admit that this lecture is a little challenging for me. Firstly, I did not get the correct answer for my survey. Secondly, I do not fully understand the Bayes Rule. I did try to watch another YouTube video on Bayes Rule, but I am still confused. However, I did learn one thing from this lecture, which is that there are many variations behind the statistics, and they can be manipulated for the benefits of some people. I need to be conscious of applying certain statistics in my arguments to avoid giving bias opinions like Rudy Giuliani did about the surviving rate of prostate cancer in the US and in England.

Yes, I understand that Bayes’ rule can be confusing. It will be a good practice to use decision trees by assigning natural frequencies and using probabilities to solve problems like this.

I think that the difficulty of Bayes’ rule was part of the point, Fengxia!

We use probability theory in our daily lives. There are five axioms that deal with probability. Probability theory makes you think of which probability in any situation. Bayes’ rule seems more complex than the rule of total probability. These probabilities deal with math. You have to cross multiple when using Bayes’ rule to find a specific outcome on the information based on probability B or A. In survey 1, dealing with the probability of a woman having breast cancer, the guest speaker uses the Bayes’. rule. When the guest speaker wa talking about using a decision a decision tree for answering a probability your being asked I felt that was sort of like a shortcut. I didn’t really understand part two of the lecture. My main take from this lecture was that we use probability in ways we would never know about. Also we should try to gain knowledge on statistics and probability instead of going off of other people opinions.

This guest lecture was very informative on probability and the different uses of it in different disciplines and in everyday life. I had understood the many theories and rules very well because of the multiple examples the professor had used. Since probability is used in every day life, people can manipulate it as well as everyone interprets information differently. We do need to check and even do our own research on the statistical data people use and see how they backed the data up with information, and making sure its reliable from the sources used to the information explained.

This guest lecture was very informative on probability and the different uses of it in different disciplines and in everyday life. I had understood the many theories and rules very well because of the multiple examples the professor had used. Since probability is used in every day life, people can manipulate it as well as everyone interprets information differently. We do need to check and even do our own research on the statistical data people use and see how they backed the data up with information, and making sure its reliable from the sources used to the information explained.

This guest lecture gave a statistical observation prospective. When thinking in terms of cancer what is the first things that come to your head? You would want the best odds to become better and go into remission. Now put into thought the different possible outcomes; probability is widely used to compare outcomes or in the case of cancer mortality rates. When data is misunderstood doing you own probability based on the knowledge you do have can help you better understand the situation at hand. Often times statistics can be manipulate to benefit certain people which does not give full insight to what the true outcomes could really be. Do your own research and understand statistics. Now ask yourself ; If you knew the full extent of risk from similar cases how likely would you be to undergo certain treatments?

I definitely agree with what you are saying this is a very good point. Its true that if I had a better understanding of the statistics of certain treatments it could change the decision I make in terms of my treatment plan.

This guest lecture was extremely informative and eye-opening to the world of probability and statistics. Right from the beginning Professor Bayaz-Ozturk mentioned that the topic of probability is interdisciplinary, applying to almost every facet of life and business. She harps on the importance of validating sources of statistics in order to gain insight to the most unbiased and up-to-date information available. The manipulation of data and statistics in today’s world can easily be accomplished and done so in a very convincing and seemingly truthful manner. Numbers and fancy words can get thrown around in order to paint a certain picture to push a certain agenda.

Finally, math that I can relate to real life! What stood out the most for me was the different methods of probability and how it relates to health care. Statistic can be very misleading especially when comparing two things that are correlated but not necessarily related. The example used in the second video that highlighted the cancer survival rates should not be compared between two different countries is important. Even if we relate it to the current pandemic, what happens in smaller countries cannot compare to what happens in America. The difference in technologies, treatment and diagnosis plays a significant role in health care and survival.

Thanks for your contribution Keisha. I agree that we need to be cautious when analyzing world heat map for covid-19.

That’s a good point, Keisha. Comparing countries is often done when people want to highlight a specific point of comparison, but all else is rarely ever equal.

The statistics shown in this video helps enlighten the situation on the manipulation of information through misunderstanding of information. Prof. Gulgun Bayaz-Ozturk explains the way that most people make mistakes in the way that they present the information given to them. She shows us that we should have a deeper understanding of statistic so that we can it is less likely to be deceived. The example that was given by the Professor showed that Phycologist Gerd Gigerenzer found a misinterpretation/ mistake in Ruby Guilliani statistical statement. He showed that it was incorrect because it was like comparing two different things. This situation enlightens the way we would normally view statistics. In most situations we should not take it as a fact when it can be construed and changed to fit one’s own perspective.

Thank you Professor Bayaz-Ozturk, for such an interesting and informative lecture.

When professor Ozturk revealed that only 21% of gynecologist responded to the survey correctly I was truly in shock. Twenty-one percent is less than if they would have taken a random guess. Even a random guess would be at a 25% rate. In health care it is very important that health care professional is knowledgeable to be able to calculate these types of probability correct. Patients’ decision making and outcome depends greatly on it. Aside for being able to correctly give the patient the probability and statistic numbers, they must take into accountability of other factors as well while doing their calculation. For example, professor Ozturk mentioned that Gerd Gigerenzer, realized the issue that we cannot compare the survival rates of those with prostate cancer within the US and the UK. Both countries are different, and they use different testing and screenings. Comparing them would be invalid because we are not comparing two countries that are similarity identical but the opposite, they are completely different. Using inadequate factors are like comparing “apples and bananas.”

Makes you think twice about fully trusting medical professionals, right? I think it also shows why, when you have an important medical decision to make, you should usually get a second opinion.

Professor Ozturks lecture really made me notice more how as a country I believe our medical practice is better then most. For example when she provided the percentages for cancer patients that overcame their illness between the UK and the US. I also find it very interesting how even in the medical field math is some how involved. Finding the probability of something using the method she spoke of have shown great results. Although false positives come into effect at times. I also think it was a great example to use our former mayor.

Professor Gulgun Bayaz-Ozturk was able to give us a better understanding of the Fundamentals of Probability Theory by explaining the Axiomatic Theory, by showing us a set of Axioms which are a set of rules. It showed us ways that some can intentionally manipulate or misconstrued information, for their own interest. These things can affect people’s lives. Seeing the professor going over Bayes’s rule in survey 1 gave a better understanding of why doctors should start applying this rule to their studies and comprehending a situation better with a person’s medical issues.

before I watched the videos I tried to answer the survey question, based on common sense reasoning only you would think its easy to guess the right answer but it turns out their is a formula/science to the answers and this is called probability theorem. It was also shocking to learn that of the 160 physician who got surveyed only 21% got the right answer, as it turns out many people are statically illiterate and physicians are no exception. the videos also made me more aware of statistical biases as stats can be construed in such away that it creates a false narrative , professor Bayaz-Ozturk used the comments of former mayor Guiliani on prostate cancer as a case study and to also make her point. in the end she concluded that becoming statically literate is the best way to guard against false narrative and misinformation because its risky to rely on others for statistically accurate data.

Yes, that is very surprising, especially because doctors have training in statistics.

Thanks to this lecture, it showed me the importance of probability theory and how it can benefit us everyday. The professor also shows how probability theory can affect the decision making process both positively and negatively. It was explained thoroughly and pretty much was easy to understand. The guest also touched on statistical illiteracy, which is when people talk about the probability of something they have no evidence to back up on. People should use statistics to give more accurate information rather than just repeating what they heard from someone else.

Thank you all for your feedback and providing your point of view. Many experiments similar to this one was conducted over the years, and in most cases, researchers came to the conclusion that the human mind is not Bayesian. Recall that Bayes’ rule revises a prior probability which is the base rate into a posterior probability after new data has been observed. In the survey provided the base rate (the probability that a woman has breast cancer) is 1%. Posterior probability is the probability of cancer given the positive test result. In the light of this new positive test, the question asks the revised probability of having cancer. As you have also mentioned in your posts, many study participants overestimated the probability of cancer. The two pioneers of behavioral economics, Amos Tversky and Daniel Kahneman, attribute this to the neglect of base rate. They claim that participants neglect the prior probability (base rate which is 1% in this example) when responding to this question, and so they overestimated the posterior probability. If we accept that the Bayesian rule is the normative rule of inference (the rule that is used to categorize decisions as rational and irrational) and the rational decision making, then any systematic deviation from that rule can be named as error in statistical reasoning, and human mind can be viewed as flawed by some fallacies.

However, I should also note that Gigerenzer and Murray (GM) criticize the idea that Bayesian theorem is rational thinking. GM discuss that we can explain a variety of human decisions by using alternative statistical theories. Human choice can contradict with Bayesian statistics and so can be named as “irrational”. However, they show that choices which are labeled as “irrational” by Bayesian statistics can be explained by Fisherian statistics. In our example, even though “overestimation” of the probability of cancer given a positive test cannot be explained by the normative rule of inference (i.e. Bayesian statistics), it may be explained by alternative statistical rules.