Online discussion questions – Day 6 (Jan 26) – Mistrusting Data
- What kind of unreliable data did you find today from Yelp, Amazon.com, or YouTube? Why do you think is unreliable?
- What are the examples of questionable data/information we can find around us everyday?
16 Responses to Online discussion questions – Day 6 (Jan 26) – Mistrusting Data
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On Yelp. com and Amazon.com I found reviews on services and products from customers and consumers. These reviews can be very unreliable because the reviews are the opinions of the people. Not all consumers review their services or products. Most of the time, consumer reviews are either strongly positive or strongly negative. These types of reviews give the wrong impression on other people interested in the service or product.
On Amazon we found customer reviews just like everyone else, but we noticed something different. We were looking at one of the new Nvidia GPU models and we noticed that the temperatures the company advertised were different from what the owners were reporting. I agree with my classmates that customer reviews can be leading and unreliable at times, but in other instances, they can be better than theoretical descriptions about same products. Everyday we can find unreliable sources of data. News reports tend to exaggerate or underestimate many incidents in society to form and sway public opinion one way or another.
On Youtube, one piece of unreliable data was the number of views a video has gotten. The number of views a video has gotten might actually be a lot less compared to what Youtube says. Youtube doesn’t take into account the number of times that people accidentally click on the video or do not watch the full video. So a video that might not be that interesting at all may appear interesting due to the higher number of views.
Everyday, we find many instances of questionable data and information. Everyday, people depend on the weather channel and weather apps to plan for the weather. While meteorologist do a good job of predicting weather, no human is perfect. In many cases, they predict the temperatures that are too low or predict rain instead of sunshine. Just because meteorologist predict it, it doesn’t mean it is true.
On Amazon, we found user reviews for products and a disclaimer that stated that the product seen on the site may not look exactly like the product received when ordered. This information is unreliable because the reviews for products can be based on the consumer’s preferences. Also, there are several different types of reviews which range from one star to five stars. In addition, if the product received in the mail is not the same as the product seen online, then the data online is seen as unreliable. Unreliable data in our everyday lives can be seen in magazines or news articles. Some magazines or news articles post information based on rumors that may be going around, whether or not they are actually real.
On Youtube, although some may not have experienced this, but I find that sometimes the titles of videos are unreliable data. The publisher of the video can name the video whatever they like, and that name might not be relevant to the content in the video.
On Yelp, we found customers reviews about restaurants around Philadelphia. We noticed there might be unreliable information, because those customers who left a negative review or a low rating did not complain about the service or food of the restaurants. Some of them are just unsatisfied of the location or the decoration.
Questionable data are everywhere in our lives. For example, the articles in the magazine or newspaper, which might have unreliable information to attract readers.
On Amazon we found that the descriptions of the products on Amazon can be considered unreliable information. A seller can post that a book is in great condition, but it is possible for them to leave out a couple of details. Another form of unreliable information can be the customer reviews. The customer reviews show positive reviews, but not negative reviews. From this we learned that the information about the product can’t be accurate if different opinions aren’t posted.
On Yelp and Amazon we found a lot of customer reviews that were all opinion based. This data is unreliable because everyones opinion is different whether it is about the taste of food, the quality of service, or the price of the product or restaurant. This is a huge factor especially when it comes to prices. One person’s opinion of what is cheap or expensive can be completely different from what another person thinks. Also when there are products for sale by the owner on sites like amazon, the customer must trust what he/she is saying about the product they’re selling. The description and details of each product could be made up and unreliable. Youtube can be unreliable when people post videos voicing their opinions rather than facts. Recently a magazine photoshopped make-up onto Bruce Jenner saying that he was transforming into a woman. Despite the magazine’s success, this information was completely made up and giving people the wrong information. Gossip magazines such as this one, blogs and other sites that gossip about celebrities are all unreliable sources because their main goal is to sell something entertaining to the public.
While on Yelp, I exmained the review comments for a restaurant in Philadelphia. This data can be unreliable because the comments give the impression that all the restaurant’s food is good, which could be true, however what’s good to someone may not be good to someone else. In addition, all these comments may be good but these comments also only add up to a small amount of people who have eaten there in the past. There could be twice as many or just half as many people who hated their food but simply chose not to comment. Unreliable data is everywhere today. On a daily basis, you can find unreliable data on internet websites, tabloids or magazines that don’t accurately tell the truth or the complete truth about a topic.
With yelp the data is unreliable because it is mainly used with customers who were unhappy with their experience and there isn’t a huge incentive for people who are happy to go and post a review. In addition, the reviews are from total strangers so I don’t understand why someone would trust what a complete stranger says.
From the numerous amounts of websites that we use to search the web many of them relay information to us that can be deemed as unreliable and untrustworthy. Data such as personal reviews, recommendations, and forums are all based on perspective rather than absoute fact. Personal reviews of restaurants, bars, and clubs from yelp are all based on customer experience. Also, youtube may make recommendations for videos “you would also like” based on your search history, which can be untrustworthy. In terms of everyday, data that is unreliable would be the weather channel or forecast. That stands out the most as an everyday assesment that has a history of being somewhat unreliable.
What kind of unreliable data did you find today from Yelp, Amazon.com, or YouTube? Why do you think is unreliable?
Most unreliable data points from these websites were reviews. This data is unreliable because you are clueless as to who is posting the review. Many people will only leave a review for a product, restaurant, or video if they felt very strongly about it (either negatively or positively), which skews the data because the average experiences are not included. Also, the people submitting reviews could have a personal motive such as they work for the company, they own the restaurant, or are otherwise financially linked to the success/failure of whatever is being reviewed.
What are the examples of questionable data/information we can find around us everyday?
Examples of questionable data we can find around us everyday in the media. A lot of data about celebrities, public officials, and high up executives is skewed to either purposely reap benefits or cause damage to a persons reputation.
The unreliable data that I found was on Yelp.com The reviews given on Yelp.com are all subjective and cannot be measured objectively; therefore, you cannot rely on the reviews given. In addition, Youtube video titles are unreliable because a video can state something in the title and be a totally unrelated video. Both of these examples of unreliable data that can be seen around us every day we just have to be able to distinguish reliable data from unreliable data in order to use the data effectively.
On yelp.com we found that many of the reviews can be unreliable. They can be unreliable for the reason that many reviews are given based on the service that they received, therefore the reviews are only based on one experience rather than a handful of times, so the data is not reliable. Every day there is unreliable data in the news, because there are reporters who report stories from their point of view rather than a neutral point of few, which can lead to unreliable information. Another example would be politics, in which people are always trying to put their competitors down.
On youtube.com it is very easy to find unreliable data. First, the title of the video may be very misleading and may not describe the video correctly. Next, the number of upvotes and downvotes may be skewed because of personal preferences, practical jokes, or uneducated actions. Lastly, comments may influence viewers in ways that they wouldnt have if the viewer watched the video without reading through the comments. In addition, the number of views is not always reliable because it does not necessarily discount the number of times the same person has watched the video multiple times.
When I was searching the web I came across couple websites like Amazon and Yelp and ended up reading and found reviews on services and products from customers and consumers. the majority of the time, consumer reviews are either strappingly positive or strappingly negative.