Section 003, Instructor: Shana Pote

Weekly Question #3: Complete by February 3, 2016

Leave your response as a comment on this post by the beginning of class on February 3, 2016. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your opinions, not so much particular “facts” from the class!

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In your opinion, what was the most important takeaway from last week’s discussion on bias and the Signal Problem? Give a recent example of how you’ve used data to make a decision, and a hidden (or not so hidden) bias that was likely present when making your decision. Did this result in a signal problem regarding your analysis and decision-making? Describe how you might have counteracted the bias and Signal Problem for your particular circumstance.

61 Responses to Weekly Question #3: Complete by February 3, 2016

  • Profile photo of Jose X Villanueva

    I think the most important takeaway from this week’s discussion has to do with the idea of bias. Usually in the cases of restaurants, the reviews are usually bad on the websites because the person felt so strongly that they decided to go and write a review. For instance, last week I went to a good amount of restaurants for restaurant week in center city. Before I went to each place I tried to get a general idea of it by reading the reviews and looking at the pictures. For one, the reviews did not do it justice. The place in my opinion was fantastic, but the reviews did not reflect it. I usually feel that people who post bad reviews expect a lot, which in turn causes them to never be satisfied. The only way to really counteract this problem is by posting a good review at a place if you had a good experience. This will help to balance out the reviews and try to make it fair.

  • In my opinion, i would say being bias. In my previous contract on working with FA-18D, i had to choose a fire team to conduct a maintenance job for a cross country sortie (flight). I was speaking to team leaders of each shop and they provided me with one of what they think is their best man for the job. I knew they were just giving me their new guys based on their ranks. I needed the job done the right way the first time, so to counteract the problem, i decided to ask them certain questions to see if they would answer it correctly. that was my way of checking to see if it was really the right man for the job.

  • The signal problem is especially prevalent when dealing with online review type websites. The reason the problem arises specifically for review based websites like yelp and amazon is because those who have drastic opinions that feel the need to post are often the only reviews. There is a large gap when it comes to the people that do not review the restaurant or product. Their opinions are not necessarily unsatisfactory, those who do not review might simply have not thought to. Whereas most people with a negative experience will look for reviewing to offset some of the frustration with a poor experience. Recently i found myself reading the reviews of a store on google maps. I then realized the review was too good to be true and assumed an employee must have written it.

  • Profile photo of Isaiah J Carroll

    The most important takeaway from last week’s discussion was how skewed marketing ads can be. We learned about how a lot of data is bias or is just a sample representation of what really is occurring. So it makes me wonder how much of what I see on billboards and TV is accurate and precise. I am a telemarketer and everyday I use data to determine what territory I am going to call. I may look at a lead sheet and scan over the customer’s name, residence, and list of referrals and based on that data I will decide rather or not I will call them. I do have a bias favoring the Virginia and Maryland areas when I am calling. This did not cause a signal problem for me and it hardly ever does because the data I use is backed pretty strongly by my results and percentages.

  • I think the most important takeaway was the Signal Problem. Data can easily be misleading and lead to inaccurate information as people who are unaffected by an event may be the people creating the most data about said event. This is exactly what happened when Hurricane Sandy hit and the majority of the tweets about the disaster were from Manhattan, which could lead others to believe Manhattan was being affected the most. The truth being, Manhattan was not affected at all. Those in New Jersey were affected the most, and had no power or concern to tweet about what was happening while they were experiencing such a disaster. The other day, while deciding where to go to dinner, I looked at Yelp reviews of a restaurant to see if it would be a good place to go. The restaurant had 4 stars and 86 reviews which led me to believe it was pretty good, and the pictures of the food looked delicious! Bias was probably used in some of the more extreme reviews, due to a really bad experience. I knew to listen to the more average, commonly shared reviews. My own personal bias in making this decision came from the fact that the restaurant was super close to Temple and I did not want to take the effort to travel farther for a possibly better meal. I could have counteracted my bias by getting over my laziness and deciding to travel further to go to a higher rated restaurant. This decision had no signal problem.

  • Profile photo of Akshat A Shah

    I think the most important takeaway is being bias. Some people just write bad reviews just because they have a problem the first time they go there. They do not give the places a second chance. For example, last week I went to Dave and Buster;s with some of my friends. Before I went i looked at a few of the reviews some customers have posted. Most of the reviews the people wrote said some bad things. However, when I went with my friends, it was amazing. The employees were nice and they got everything we asked for. The reviews did not do that place any justice. We had an amazing night and I would have given that an good review.

  • One of the most interesting takeaways from the discussion for me was how during the hurricane, more tweets came from Manhattan rather than New Jersey, the place that was actually hit by the hurricane. Recently, I used review data from yelp to make a decision on where to live next year. In many of the reviews, many people were either extremely ecstatic about the place or extremely dissatisfied with the place. One way to counteract the bias is ask someone who lives there in person, and visit the place in person.

  • Profile photo of Kashif Hasan Malik

    In my opinion, the most important takeaway from bias and the Signal Problem was that bias can expose you to only certain information, not all of the information available and can make you make your decision incorrectly. For example, If I wanted to buy a video game and I went to look at the reviews to see if it was actually a game I wanted to get or not. Bias could be the genre of the game because some people might review sports game higher than action games or vice versa and that’s what I would see on the reviews or my preference can get in the way of making a clear decision. This did result in a signal problem because more people favoring action games could’ve gave bad reviews on a sports game and I would’ve seen so many bad reviews that I could’ve thought the a lot people dislike it when it’s not a clear representation of all people reviewing this game. One way I could have counteracted this bias is to go on different websites which promote games I wouldn’t normally see and that could expose to information I wouldn’t normally have seen.

  • Profile photo of Ashley Charlton

    The most interesting thing to me was how bias is every where. Bias can lead to you only receiving certain information or even getting the wrong information. For example, my roommates and I wanted to go out to dinner and to a place none of us have been to. We all looked up the reviews and there were so many different reviews that you could tell some were definitely bias. Everyone has different experiences and opinions. With reading the reviews we trusted the people who had made multiple posts and seemed reliable, not the people who had only posted once because they felt so strongly about their experience.

  • I think the signal problem presents a huge vulnerability in data collection. I think the most important thing to take away is that when someone is collecting data they can never be to careful because more often than not, a bias will occur that one does not recognize. One personal example is when I was buying groceries. I wanted to get the best deal on sugar, so I looked at the lowest price. What I realized is that I would be getting a better deal on a larger bag with a slightly higher price, than the smallest bag with the lowest price. I would say this resulted in a signal problem for me and others looking to buy sugar. On way to counter act a signal problem it to not take everything at face value. There is always more than meets the eye so one must be careful when using data to make decisions.

  • Profile photo of Katherine Braccio

    Personally, I think that bias within data is much more harmful in the long run, especially considering that a bias in data isn’t considered in everyday activities. Recently, I’ve been looking for a pair of boots with very specific qualities: short, flat heel, and a light brown suede. I always use the same browser, and stick to the same few retail websites. As my search for these boots have continued, I have seen an increase in ads for short boots as well as for the stores I have been searching. Not until today did I realize how prominent the Signal Problem is: I was searching once again for boots under $150, and the results of my search were sorted by featured items. However, these featured items all fit within my previous search terms. Although this is not a negative issue, the same bias and signal issues can be easily transferred to more large scale problems.

  • Profile photo of Shuyue Ding

    I personal very carefully about the information I need to use to make decision. I cannot think of any decision that influence by bias, but I believe it does influence me. It is possible that I just did not notices. I believe bias exist everything and it influence a lot of people’s life. I will pay more attention into it. However, Signal Problem is very annoying. Every time I brought or view something online, it shows up many similar ADs about those items when I view other websites. It is just so annoying, and I could not figure out some way to avoid it.

  • Profile photo of Alice Nguyen

    I think the most important thing about last discussion is how easy it is to fake trustworthiness. It might take a while, but when just putting up a profile photo that might not even be real makes the user/reviewer’s words a little more reliable, there is no telling what a dedicated liar can accomplish. Of course, most people don’t have the time nor effort to pull off such scams, but it’s a scary thought nevertheless. As for a recent example, just a few days ago I was looking for datasets for the assignment. I have noticed that I kept looking for data in a few cities I know like New York, Columbus or California to compare with Philadelphia. In addition, because I like living in Philadelphia, I usually look for information that confirms my beliefs and ignores datasets that has Philadelphia being worse off than some other places. I only fixed this problem by looking back after I was done, changing my hypotheses to a fair mix of positive and negative opinions, and look for corresponding datasets.

  • Profile photo of Matthew Major

    In my opinion, the most important takeaway from the discussion was that you truly need to look into the specifics of the data. Draw your own conclusions about whether or not the data is reliable, because there is a large amount of unreliable data out there. Prime example is when I went to the fish store yesterday to purchase pet fish. I went to this large aquatic pet superstore that had a great selection. However, when I searched it I realized that it had a 2.5/5 star rating, and that cautioned me. After looking into the reviews, I had realized that the bad ones were quite biased based off of dumber complaints that people had. Issues such as overpriced food caused for a 1 star review, which I agree isn’t necessarily great, but is 1 star really necessary? Therefore, I went to the store anyway, and had a great experience! Awesome selection and the employees were very helpful. Always remember to truly look into the data, and try and figure out the true reason that it is presented in that manner.

  • Profile photo of Colin Kelly

    In my opinion, the most interesting topic from the discussion last week was how often bias can be found in reviews. After searching for reviews about a couple of different places on Yelp (such as Temple, restaurants, and my previous place of employment) I’ve noticed that there was many reviews that had either a one star review or a five star review, with there rarely ever being any reviews with a rating in between. To add on to that, many of those extremely one sided views came from people who have barely done and reviews before, and for some it was even their only one. This made me realize that those reviews can’t necessarily be very trustworthy because one person may have been judging their opinion based on only one visit to a location. In fact, I even saw a one star rating for Temple on Yelp from a student who was angered because classes weren’t cancelled last Monday due to the snowstorm. The talk about bias has definitely made me pay close attention to reviews and make sure I only take them with a grain of salt rather unless there is a consistent number of reviews saying similar things.

  • Profile photo of Julianne Johnson

    I think the most important takeaway was the institution of bias. Personally, it would take the absolute worst and best experiences imaginable for me to write a review about a place. If I did so, it would be extremely biased, either staunchly in favor or in opposition of the restaurant. Most reviews of restaurants are similar, biased towards favor or opposition. My opinion/review would not be very trustworthy because my experience at the restaurant is exclusive to me. Thus, a lot of reviews should be taken with a grain of salt, because you are not guaranteed the same bad or good experience as others.

  • Profile photo of Kennedy Frances Price

    I believe the most important takeaway from the discussion was identifying trustworthy data. On sites like Yelp, many people are going to be biased one way or another whether it be for a school, restaurant, or retail store. I find the reviews that explore both the good and bad of a place the most helpful. A rating that is in the middle rather than towards an extreme is a review I am likely to trust more. Recently I have been searching for a place to live next year, and I have been reviewing each complex on whoseyourlandlord.com. Many reviews on this site are extreme opinions, more so negative comments than positive. I am using to this data to understand the positives and negatives of living in each of these complexes before I make a decision.

  • Profile photo of Craig Jacob Kestecher

    I think the most important takeaway is the personalization of information, due to signal problems and biases. Recently, I used data and reviews, found on google and yelp, to find nearby jiu jitsu training facilities. I have a bias because at home I trained at a Gracie Jiu Jitsu facility, and as soon I saw a Gracie facility nearby campus, I stopped looking at other facilities. The signal problem I could have encountered would be the elimination of other types of martial arts from my search e.g. boxing or wrestling. My interest is mainly jiu jitsu, for example on YouTube my recommended videos are solely jiu jitsu based. I could have counter-acted my bias by searching for other martial arts. Also, to counteract the signal problem, I should have erased all my internet history and broadened my search terms.

  • Profile photo of Craig Jacob Kestecher

    I think the most important takeaway is the personalization of information, due to signal problems and biases. Recently, I used data and reviews, found on google and yelp, to find nearby jiu jitsu training facilities. I had a bias because at home I trained at a Gracie Jiu Jitsu facility, and as soon I saw a Gracie facility nearby campus, I stopped looking at the other facilities. The signal problem I could have encountered would be the elimination of other types of martial arts from my search e.g. boxing or wrestling. My interest is mainly jiu jitsu, for example on YouTube my recommended videos are solely jiu jitsu based. I could have counter-acted my bias by searching for other martial arts. Also, to counteract the signal problem, I should have erased all my internet history and broadened my search terms.

  • Profile photo of Elena K Cipparone

    I think the most important take-away from last week’s discussion is that you cant necessarily believe that the information you come across is completely accurate. Especially in the case of the Internet, where people are free to post/edit – there are bias’s present. I recently used the Google reviews and ratings when looking up a restaurant. I realized that I simply look at the star rating along with the number of “$” indicating the restaurant’s cost, whether it is cheap, moderate, or expensive. I never really took into account that what may be inexpensive to someone may be expensive to another. I also just took anything above a 4 star rating as a good restaurant, and was disappointed when I disagreed. I did not take the time to look at the number of reviews – one 4.5 star review could end up being the owner of the restaurant, which would have indicated, to me anyway, that the restaurant is really good. Next time, I will likely examine the number of reviews and maybe even read a few before making a decision based on the metadata summarized on the Google search page when looking up a restaurant.

  • Profile photo of John W Forsythe

    In my opinion, the most important takeaway from the discussion last week was that not all data is entirely accurate. For example, reviews are loaded with biased data and can’t be credited as trustworthy, reliable data forms. It seems as though most reviews on Yelp and Google are extreme opinions, where individuals post either very negative or positive reviews, and give extremely high or low ratings. These reviews are based off of individual’s personal opinions and experiences and it seems as though only people with extreme opinions take the time to write reviews on these websites. It is very rare to find reviews and ratings that are in the middle, and I personally believe these reviews are most credible. The next time I look to buy something online, I will be certain to read a couple reviews to decide whether or not the data is reliable and not just simply filled with extreme ratings and reviews.

  • Profile photo of David J D'Angelo

    Last week’s discussion brought up many good points about data and how a lot it can display a bias. For example, google has an option to review a place, and one review for Temple University was rated one star. The person who posted the review was very upset about how Temple University students had class on a very snowy day, and that caused him to leave a bad review. One bad experience at Temple caused him to say that Temple University is unsafe and he doesnt feel comfortable having loved ones go here, which is completely untrue.

  • Profile photo of Mark Anthony Negro

    I found last week’s readings and discussions very interesting, especially the talk of the different bias in data. This is very important when using data because there are many underlying factors not even considered that could have a significant effect on data. Last week, when my girlfriend and I were deciding where to eat, we took to yelp to decide. This can be a fantastic resource but it also can be filled with hidden bias. It is very possible accounts are fake and are used for business owners to laude what’s theirs and to manipulate reviews for competition. Realizing this, we only paid attention to profiles that seemed credibe.

  • Profile photo of Jordan Timothy Motter

    The biggest point that stood out to me this past week was that data can be very untrustworthy in certain situations. our discussion on the Yelp and Google reviews proved that to me. Last week during restaurant week I was picking a place to go and eat. Instead of using the reviews on yelp I asked a few close friends from around the area about a few different restaurants and got their opinions on the matter because I knew that they would be unbiased and truthful in telling me which restaurant to go to. I also got more in depth with them about what they ordered and suggested. Their reviews could also have a certain bias but I trust them more than I trust the Yelp reviews.

  • From last weeks discussions on bias and the Signal Problem, I took away the importance of counteracting these issues. I think it all comes done to understanding what is reliable and what is not reliable. Recently, I have been looking for a good nail salon to try in the area and have been looking at reviews. Like reviews for restaurants and other establishments, the reviews are not always trustworthy. I tend not to be as trusting of what I see online and believe the only way to really know is to experience it yourself. However, reading reviews I am swayed to try certain places over others.

  • Profile photo of Brandon K Shaffer

    The most important strategy that I took away from our class discussion on the Signal Problem was being able to differentiate the truthful reviews from the bias reviews. About a week ago I was looking online for a place near by to get my haircut. I found Diamond Cutz and looked though a few reviews. Every single review I looked at was very positive except one. The customer gave a poor rating and no explanation as to why they gave the bad review. I took this as a bias review since it was the only one of dozens and went to Diamond Cutz anyway and it turned out to be a great place!

  • Profile photo of Rahsaam K Ray

    The most significant knowledge that i gained from last week is that not all data can be trusted reliable data and not all data correlates with each other. In some case studies a lot of information is collected but not all of the data is useful to solve the problem at hand. Signal also pays a key factor in what data and reviews are valid as well because we have to take into consideration who is delivering the data. Websites like yelp and amazon can sometimes give falsified data because someones bias could have a profound effect on their review of the product or service.

  • Profile photo of Thomas Alexander Stenberg

    I believe that both signal problem and bias were two very interesting topics that we covered last week. It’s very evident that individuals can create a signal problem (especially with review based sites) as they are generally going to give an extremely bias review based on a possibly good or bad experience they may have had; if everyone instead were to review no matter their experiences we could have much more reliable data. To counteract issues like these we need to find a way to incentivize people giving their reviews regardless of experience or bias.

  • Profile photo of Gabriella C Baldini

    The most important takeaway from the discussion for me was that you can’t escape bias, and that it’s better to get used to learning how to identify untrustworthy data that might stem from bias. Many reviews you see, whether they are for restaurants, products, services, etc., are comprised of either glowing reviews or nasty ones. It can be difficult to determine which of the “glowing” reviews are trustworthy and which were written by the company owner’s sister-in-law or great-nephew. It can be equally tricky to identify which of the “nasty” reviews were written by people who have a flair for the dramatic. I recently purchased a new laptop, but beforehand I researched extensively on reviews, consumer reports, etc. I definitely had a bias towards Macs, however, and found myself comparing everything against the laptop I really wanted. While I still took the reviews and reports seriously, I will admit that I was trying to research others just to confirm my decision to buy my first-choice Mac. Counteracting bias in general requires that we take everything with a grain of salt, and really the only way to be sure about most things is to try them out for ourselves (restaurants, products, and services).

  • Profile photo of Junaid K Farnum

    In my opinion the most important aspect that I had got from last week’s discussion on bias and the Signal Problem, is that finding good quality and reliable data relies on firstly good information and also, while not always necessary but greatly appreciated, the effort and time spent from an individual in making and delivering the said data. Many times short lived, no-name reviews on restaurant reviews for example, doesn’t seem credible because of its illegitimate nature. However, when we see a full name, and a carefully explained review noting positives and negatives we tend to value that review over others, even though a bias will be present. A recent example on how I used data coupled with bias in my decision making was in choosing certain courses for Spring semester. Using the data on sites like RateMyProfessors.com sometimes prove accurate when choosing courses that certain professors teach, while at times they might not however my bias comes into play when I see certain reviews I like given what I mentioned above where an individual takes time to give a detailed explanation to how good or bad a professor is. Sometimes /I find the reviews completely contrary to what I read, causing a signal problem with the ratings/ or data that was accumulated for a professor. A way to counteract my bias in regards to this signal problem would be to give a detailed review myself or to purposely enter a class where the professors rating is low and see for myself if the data is an accurate representation.

  • Profile photo of Alexandria M Freeman

    During last weeks discussion on the bias and signal problem I think the most important takeaway was that most data is unreliable according to bias. Just from looking at the different reviews of Universities in our area I found that most reviews were from current students or alumni with only positive comments which is extremely biased. I have been noticing bias in reviews more after this discussion. My friends and I have been trying to find new restaurants to eat at but, looking at reviews first and mostly finding positive reviews featured first even if there are more negative reviews. this just makes me realize I must take everything into account because the internet filters my view of the world without even knowing it.

  • Profile photo of William G Roman

    The most important takeaway from last weeks discussion was how bias can make many reviews unreliable. The reviews are often unreliable because many people write a really positive review or a really negative review based on one experience. You can’t really decide how a your experience at a restaurant will be based off another person’s only time at that restaurant. For example, over the weekend I decided to look at some reviews on yelp for restaurants in center city. Sure enough, there were numerous one or five star reviews about the restaurants. I chose to go to a restaurant that had a few one star reviews for different reasons to see if I experienced the same problems. I had no reason to complain because both the food and service were great. The best way to counteract a bias you may find on a review site like yelp, is to go to a place in person or ask someone who has been there multiple times.

  • I think the most important takeaway from the discussion was the idea that bias can drastically affect the information you get out of data. After reading some of the restaurant and Temple reviews on both Google and Yelp, it was obvious that some negative reviews were based off of seemingly trivial factors or individual bad experiences that didn’t necessarily reflect the business overall. I recently moved out of my house and into an apartment so that I could be closer to school and avoid the long commute from my family’s home in the suburbs. Part of the process was coming up with a monthly budget so that I would be prepared to handle rent, bills, and other expenses with my current income. I used a database called Numbeo to get a general idea about the cost of living and budgeted accordingly. Now that I am living off of this budget, I realize that I have a lot more money than I accounted for, mostly due to overestimation of what food costs would be. My spending on food is significantly lower than the average American cost, leading me to believe that the numbers are a bit biased. I think this is because Numbeo accounts for people who tend to be more liberal in spending, or maybe the database is simply based off of general retail prices without accounting for possible sales and discounts. Either way, this signal problem worked out for me as I now have extra funds to live lavishly and eat more than just Ramen noodles.

  • Profile photo of Michael Lawrence Carey

    I believe the biggest takeaway from last week’s discussion was the trust in data. Not all data received is necessarily correct and accurate via bias, that leaves us, the consumer, to try and fact-check the data if we are not sure if it is correct. Data i’ve used recently was purchasing a MacBook Pro. I did a lot of research about the product and its protection against viruses as my previous laptop succumbed to a violent virus resulting in my loss of all my personal documents. I checked many different reliable sources to figure out if a Mac was reliable and, according to all my sources, the MacBook was far more reliable in terms of virus protection and performance in comparison to my previous Dell laptop.

  • Profile photo of Jake Montana

    The most important part of last week’s discussion was bias. While reading through reviews of places to eat the best cheese-steak, bias was very apparent. The reviews were very extreme, either very supportive of how good the sandwich was or how poor the sandwich was. You never really know how good the cheese-steak is until you experience it for yourself. The best way to counteract a bias is to read reviews from people with profile pictures and people that may give 3-4 star reviews. Reviews that may seem long also might cover every aspect of what is being reviewed so these are more trustworthy.

  • Profile photo of Erica Corinne Rudy

    I think one of the most important takeaways is that the facts we are finding on the internet may not always be “fact.” Due to our locations, preferences, past searches, and other factors, the information that we are searching for is tailored to what we ‘want’ to see, versus what may be more true or something else. I have had this happen to me when searching information for a race/ethnicity class – I do not identify as a political party, as I have varied opinions on different topics (most would probably consider me to be more liberal) and, when searching for facts about welfare and who receives them, I would receive statistics that would upset most “republican” ideologies about welfare.

  • Profile photo of Ryan C Gibbons

    In my opinion, the most important takeaway was that it is extremely important to make sure you know where your data is coming from, so you can identify any potential biases that could be associated with it. I recently made a decision to avoid purchasing a video game that I had been looking forward to playing. I used a review from a site called IGN to help me make my decision. In this situation, IGN actually did a good job of avoiding biases by hiring someone unrelated to the company to do the review, because one of IGN’s main reviewers had been involved in the game as a voice actor. Had an IGN employee written the review, they may have had a bias towards the game and given it a positive review that it did not deserve in order to help out their co-worker and give the game positive publicity.

  • An important takeaway from last week’s discussion on bias was that people who have different experiences may give low ratings or high ratings on yelp based on whether the workers were nice and had good service that day or if workers were rude to their customers that specific day because of their stress. An example of how I used data to make a decision was when I went on yelp, I experienced bias on some of the comments in the reviews because some reviews had three-star ratings when their comments positively described the restaurants. It also tells me the high standards that some people have because they possibly believe that there is a no perfect five-star rating that should be given to any restaurant unless it is actually perfect. It did result in a signal problem regarding my decision-making because ratings were could have been misleading and I would have counteracted bias by first off, trying out the food at the restaurant myself a couple times to make sure food and service were great or terrible for all of the days I have eaten there.

  • Profile photo of Tae Shin

    The most important take away for me from the conversation we had on data biases and signal problems is that you have to take everything with a grain of salt. Do not believe everything you see because more data are usually needed. For example, when looking at Yelp one has to account for the user who posted, the amount of reviews the search has, and the content of the reviews. Recently, I used data from a annual report in order to formulate an idea on a company’s future progression. The bias here could be the fact that the company is the entity that wrote the annual report, therefore, I would have take biases into consideration.

  • Something I took away from the discussion about bias was that when considering reviews or doing research on something, knowing how to spot bias is very important. When I was recently researching a new car to buy, I used lots of data comparing models and I read dozens of reviews. People include bias into reviews subconsciously, but some more than others. I had to recognize which reviews were realistic. If I noticed patterns or recurring data in reviews, the review is most likely credible. Categorizing this information and removing the unusable reviews made picking a new car much easier.

  • Profile photo of Samantha Elizabeth Simmons

    I think the most important takeaway from last weeks class was the biases we face when collecting data. Particularly, we face many biases with data retrieved from online sources such as yelp. Yelp can be a very useful piece of data. However, the data found on yelp can be subject to many biases. We were talking in class about people’s emotions being a cause for bias on sites such as yelp. If you go to a restaurant after having a bad day and you also have a bad experience at the restaurant, maybe this was just the end to a really bad horrible day and your emotions made you a little on edge and therefore made the experience worse than it really was. That would cause a bias in your comment that others read when looking for data on that particular resaurtant you visited. This is why data that comes from sites like yelp can’t ever be depended on completely.

  • Profile photo of Devon D Harris

    Along with everyone else, I agree that bias is a topic that I stuck with me from the lectures. Biases are everywhere and we cannot escape them for as long as we are living, but it’s biases that can lead to misleading or irrelevant information. For someone who is always on the move or always trying to look for new restaurants or articles to read I subject myself to data and biases every day. From the lecture, biases have taught me a powerful lesson…. question everything! I’m now looking back at all the times I would tell friends about new restaurants, places to hang, or articles I’ve read and how certain I was that the restaurant or place was so cool, or how much I learned from the articles and it is probably a 150/50 chance that some of information I interpreted was either false or not credible. This makes me aware of what I put out into the search engines and to look for more resources about similar topics if I want to really invest my time trying to go there or learn about it. Not that what I have read was pointless, but it skewed my judgment because I believed that was the only or popular source. Now I know it is better to have seven different perspectives than one.

  • Profile photo of Xiaoxu Liu

    I think the most important takeaway from last week’s discussion was that bias is everywhere in our lives. In last week’s class, we found bias on Yelp and in Google reviews, and we discussed how biased those reviews can be. In my opinion, bias are unavoidable, because in fact, reviews are very subjective ideas based on personal experiences. For example, I once went to a nice restaurant with a friend, but we argued during dinner, which really influenced my judgement, so if anyone asked me about that restaurant, I would give four stars though it might be worth five stars. Now I try to counteract the bias by only describing the facts. And although it is hard to eliminate or escape bias, I think as long as we are aware that most data are biased, we can drop the misleading data and get the “right” information.

  • In your opinion, what was the most important takeaway from last week’s discussion on bias and the Signal Problem? Give a recent example of how you’ve used data to make a decision, and a hidden (or not so hidden) bias that was likely present when making your decision. Did this result in a signal problem regarding your analysis and decision-making? Describe how you might have counteracted the bias and Signal Problem for your particular circumstance.

    Something I find to be of significance from last weeks discussion in reference to bias and the Signal Problem is that regardless of what one reviewer states, there is likely to be another reviewer who begs to differ. I recently Googled a local take-out place’s phone number I have been going to forever. I was looking to call in and place my order. In addition to being directed to the number, I found ratings and comments on the restaurant, too. After discussing this topic in class, I started glancing over the reviews and found many negative remarks and complaints about the quality and atmosphere of the restaurant! Of course everyone is entitled to their own opinion, but I was surprised to see so many people state their dislikes about a place I could eat at every day of my life. I will think twice before going by online reviewers’ comments when looking for a new place to eat or check out.

  • I believe that the most important take away from our class discussions is how apparent bias is in any type of review. Seeing as how so many people rely heavily on the reviews of their fellow consumers when making purchases, it is important for them to find unbiased, but still truthful reviews. I like to research movies before I go and watch them, and I have found a few sources that really have done a great job of pointing out the people that lack a bias in a majority of their reviews. Often these people are paid, and I believe that the existence of a paycheck is what really pushes these people to keep an unbiased opinion on whatever they are reviewing. It is hard to sift through what is fact and what is bias though, and like the ocean, a lot of bias reviews hold a lot more life underneath what lies on the surface. one bad waiter can get a restaurant a bad review when they would otherwise get a great review if it were not for that one bad waiter. Some people have even written bad reviews for places just because they were having a bad day, and decided to take it out on someone/something else. This is why it is important to decipher what is fact and what is biased.

  • I think that one of the most important takeaways from our discussion last week was the Filter Bubble. I never knew that they could/would do that to us! A decision I made recently that I had to research for was the type of drum set I wanted to get. I looked at reviews on the website as well as videos on Youtube so I could see how they sounded and judge for myself. A bias that became present on Youtube was the publishers of the content I was watching about the drumsets. The people who made the drum sets were posting the videos, which only involved good things about the drums. When reading on the internet about them, from forums to places where they were purchased, people had other opinions about certain features that were highlighted in the videos. I began to not pay as much attention to the videos and started focusing more on the written reviews due to the signal problem at hand. The way I counteracted the bias and Signal Problem for my case was that I watched videos of people playing the drum sets to songs rather than just playing to review them. To steer away from user bias due to their drum skills I watched various players on the same sets to ensure I found the right ones. Through this experience I learned that the best data speaks for itself. I also learned that less bias in data leads to more confident predictions and outcomes.

  • Profile photo of Alexander Somers Greene

    The most important thing that I learned in last weeks discussion was that bias is opinion based. I know that sounds redundant but you have to remember that you are not the people reviewing this topic. Even though some people may feel very strong one way or the other, when there a multitude of opinions you get see a general consensus about a certain restaurant, game or show. Problem is is that you are not part of the general consensus and may have different tastes then the majority of the people reviewing the item. When i went to look up reviews for a new show that came out this season to see if it was worth picking up, the majority of reviews said the show was boring, slow and generic. Thankfully,the summary of the show was enough for me to give it a shot and after watching the first three episodes I thought the show to be perfectly paced for what it was aiming for and unique when compared to other shows in its sub-genre. The way I personally counteract bias is by reaching out and getting opinions from people like me, and if i can’t fin anyone like that to give me an opinion, try it myself.

  • Profile photo of Kenneth Kirk Killian

    I think that one of the most important takeaways from our discussion last week was the Filter Bubble. I never knew that they could/would do that to us! A decision I made recently that I had to research for was the type of drum set I wanted to get. I looked at reviews on the website as well as videos on Youtube so I could see how they sounded and judge for myself. A bias that became present on Youtube was the publishers of the content I was watching about the drumsets. The people who made the drum sets were posting the videos, which only involved good things about the drums. When reading on the internet about them, from forums to places where they were purchased, people had other opinions about certain features that were highlighted in the videos. I began to not pay as much attention to the videos and started focusing more on the written reviews due to the signal problem at hand. The way I counteracted the bias and Signal Problem for my case was that I watched videos of people playing the drum sets to songs rather than just playing to review them. To steer away from user bias due to their drum skills I watched various players on the same sets to ensure I found the right ones. Through this experience I learned that the best data speaks for itself. I also learned that less bias in data leads to more confident predictions and outcomes.

  • Profile photo of Brittney Michelle Pescatore

    I think the most important takeaway from last week’s discussion was to watch out for bias reviews from online websites. It is important to keep in mind that a review may just be based off of one really good or one really bad experience at a certain place.Also, I learned that I should not base a decision off of just one review, I should read multiple reviews. For example, a few days ago I wanted to buy a product from a seller on Amazon, but first I wanted to read reviews on the seller to make sure he or she had a credible reputation and would send me the real product I was looking to buy. Some reviews were good and some were bad, but there was definitely a bias in the responses because most of the reviews were based off of just one interaction with the seller.

  • Profile photo of Joshua J Affainie

    I think that the most important takeaway from last week’s discussion was that bias is everywhere in our lives. In last week’s we found that we can find reviews on either Yelp or Google review. In my opinion, bias are certain because reviews are very smart ideas based on personal experiences. For instance, before I go see a movie I will like to read other people’s comments to see if the movie is a good movies and if worth my money.

  • Profile photo of Sakeena A McLain-Cook

    The most important take away I got from class last week was the idea that we must be more careful in looking for bias in the data that we choose to use. While looking at the reviews in class from Yelp and Google I came across a lot of sketchy reviews. When me and my friends were looking at reviews for this restaurant recently I was paying most attention to the negative reviews because I had previously had a bad experience myself which is showing bias. I could have had my friend look over the reviews knowing that she had never been to the establishment before.

  • Profile photo of Prince Patel

    The most important take away from last week’s class was that I got a good in site of filter bubble and how search engines and other popular websites track our activity to sell ads and make money. I also discovered that the enormous amount of bias on the reviewing websites creating signal problems. Before last week’s class I looked at review sites as completely positive and reliable. After actually analyzing popular sites like google and yelp I was able to figure out the real deal behind reviews. Now I can get a good sense of idea identifying biased reviews. We recently googled a good place to get a haircut nearby as my friend wanted to get a haircut. After looking up some places nearby and reading review about them on google, we were able to identify the false, misguiding and biased reviews. analyzing little things like total number of reviews and the patterns of the reviews, finally we were able to sort out the best option. This would have been impossible if we would have trusted all the reviews out there.

  • I think the most important takeaway from the last week which is related to the bias of data. When people use Google Map and plan to go to dine out somewhere, they are always tend to look up the review and star levels of the restaurants,then they will make a decision and choose which one they will go. It obvious that people judge the quality or features of restaurant by looking at the other consumers’ feedback of these restaurant as available data. I also remembered that I went to “SOHO Cafe” with my friends since it was highly recommend by my friends. However, I found that someone vote 1 star for this restaurant, which means that this person doesn’t think it is a good restaurant. By comparing to the other reviews on google map, we think that data is unreliable and then we decide to go there without hesitation.

  • Profile photo of Alissa N Smith

    The most important takeaway from last weeks discussion would undoubtedly be the issue of bias when it comes to data. With this I specifically thought of how bias plays a huge role in me choosing my classes for the semester. I usually finalize my decision on whether or not i’ll remain registered for a class based on what I read about the professor on a rating website. I can honestly say that I trust the opinions of my fellow students enough to change the time and even the day of my class if the professor does not get a good rating. I depend on the data provided from that website highly every semester and would feel genuinely anxious about how my classes would pan out without me knowing a bit about the professor beforehand. There have been times when I’ve tried to overlook certain biases that I see within the information given on a teacher just because a certain class may fit flawlessly into my schedule, yet if it ends up being as bad as stated, i move on to the next class.

  • The most important takeaway from last week’s discussion on bias and the signal problem, in my opinion, was bias. My friends and I have spent the last few days searching for restaurants to make a reservation at for one of our friends’ birthdays. However the reviews for the restaurants are all over the place, and often very one-sided. One thing that I noticed about the reviews are that they either loved it or hated it. This is most likely because only when you feel strongly enough about a restaurant, would you go and take the time to rate it. I doubt, while there are some that might, that people write reviews for every restaurant they go to, at least I know I don’t. Most people don’t rate a restaurant when it has decent service, because that’s what is expected of all restaurants. It’s only when the service is absolutely spectacular or terrible for some reason that the customer feels strongly enough to write a review. It’s for this reason that I don’t solely rely on reviews, and often have to just go see for myself.

  • I think that the most important take away in this is that signal problems and biases skew data. In other words, signal problems and biases can create mistakes in data. For example, when I was picking somewhere to eat, I used Yelp. I typed in Mexican Places. I just automatically went to further one because I did not like the neighborhood in the other close review. The restaurant that I did not chose had better reviews. However, I did not go simply because of the neighborhood. Therefore, I was showing a bias. I think this may has resulted in a signal problem because the restaurant I did not go to was in an upscale neighborhood. Whereas the one I chose was more lower middle class. Maybe the same thing happened with the pothole article. The people in that neighborhood did not have access to internet to write a review. I could counteract the signal problem by writing a review for the place with less reviews because I have access to the internet. I could have counteracted the bias by choosing the other restaurant regardless of neighborhood.

  • The most interesting takeaway from the class discussion for me was how a lot of the data we encounter are bias. I typically rely on reviews before purchasing a product. But I also make sure not to make decision based on reviews because everyone has different opinions. One way I counteract the bias is by looking at different website reviews or possibly walking in the restaurant myself.

  • Profile photo of Sunny W. S. Tam

    I think the most important takeaway from class is the importance of online reviews. I grew up in a time with the internet thus the ability to rate items online. An example is anytime I buy something online. I will always check reviews online. These biased opinionated reviews will always skew me making the purschase.

  • Profile photo of Sergio Campos

    In my opinion, the most important takeaway from the class discussion is the topic of biases. Recently I have been trying to coordinate a birthday party for a close friend. I decided to use Yelp and other review sites to find a good restaurant to celebrate the cause. After reading many reviews i started to notice a bias trend that people either loved or hated the places they went too. This caused a problem in my decision making because the data was skewed. Inevitably I chose the restaurant with the most star reviews.

  • Profile photo of Nancy Nam

    What I took away was the importance of realizing that signal problems exist. I never really though about it until you mentioned the example about hurricane report. It makes absolute sense. Data can be easily skewed due to many reasons so know the source and analyzing it careful is important. A recent example of how I’ve used data to make a decision is when I decided to go Vegan. After watching a few documentaries on Netflix and being exposed to the data on the environmental impact our daily eating habits has I made a decision to stop eating meat and dairy. The data they emphasized in Conspiracy may potentially be biased since they were trying to persuade their viewers in one form or another. Because the numbers where so large (e.g they mentioned that it takes 1100 gallons of water to come up with the average portion of beef an american eat daily) I actually did my own research and found that they weren’t far off. This being said I think bias and signal problem in data is really important point to factor in when analyzing data from a large source.

  • One of the most interesting takeaways from the discussion for me was how tweets came from Manhattan is more than New Jersey, the place that was actually hit by the hurricane. Recently, I used review data from hotel.com to make a decision on where to travel next year. In many of the reviews, many people were either extremely ecstatic about the place or extremely disappointed about the place. One way to counteract the bias is ask someone who actually lives in the place and visit the place in person.

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Office Hours

Shana Pote (instructor)
By appointment only - 9:10am-9:45am, M/W/F, Speakman Hall 207H. Email shana.pote@temple.edu to set up appointment.
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