Poor, Poor Scientists
As innovation comes directly from the scientists, Scientists are put under massive amounts of pressure for publishing. This pressure to publish has directly resulted in the ever-growing publication rates that seeminlgy has no end. With this massive influx of studies, there is a large portion of studies that are partial truths due to many different biases that scientists are forced to work through, intentional or not. The reason why there is so much potential for bias is due to the fractured system that scientific studies are based off.
Due to the emphasis of quantity over quality, for both payments and value, scientists are morelenient to not publish the full potential of what studies could have achieved. As scientists are essentiallyforced to focus on the number of intriguing thesis they make instead of quality and accurate studies,more and more faulty studies start to accumulate. To combat this, replication tests are very valuable asthey attempt to retest the study exactly in order to test the study’s validity. These tests are essentially afail-safe, where another scientific group that is independent to the original does everything that thestudy did to see if it produces similar results. Erick Turner from the FDA spoke about the replication testsheld in 2008. The FDA retested 74 studies that proved the effectiveness of numerous FDA-registeredantidepressants. From the replication tests, they found that 23 of them did not even have evidence of publication, which left 51 studies to examine. It was reported that 48 of those 51 studies that were leftoriginally showed positive results, yet when the FDA concluded the replication studies they found thatonly 38 studies out of the original 74 had positive results, thus completely disproving studies that were now found to be selling ineffective antidepressants.
If such a test is so valuable to validate incorrect tests, then there should not be so many tests thatpeople can view where the study essentially publishes false claims. Sadly, these faulty studies are unlikely to be corrected as there is no incentive within the scientific community to replicate the tests. Even though the FDA made replication tests, the company is not a good representation of the entirety of the community as the FDA is a government funded organization whose primary focus is to regulate issues such as the biased studies. This is known as the replication crisis.
As noted before, scientists’ payment are incentivized to push the claims of whatever will help their career. If the scientists are able to sustain themselves using replication test, researchers would have used these replication tests. However, there is no monetary value for replication tests so scientists avoid the very test that helps counteract faulty claims. As scientists are only human and will tend to prioritize their own living for the expense of integrity, they are forced to push plentiful theses for money and do not focus on retesting as there is no monetary value for validating what someone has already stated. This phenomenon essentially eliminates the fail-safe that is made to get rid of the faulty studies, which means that the number of studies that are essentially inaccurate are going to steadily increase with little resistance.
This phenomenon is very detrimental for the future of science. In the article, “Pressure to ‘Publish or
Perish’ May Discourage Innovative Research, UCLA Study Suggests,” author Phil Hampton discusses a study lead by Jacob Foster that measures the risks and innovation studies take and the implications that it makes. Foster found in biomedicine and chemistry that more than sixty percent of the studies that were analyzed showed no new connections. This essentially means that innovation is slowly grinding to a halt due to the flawed system. As scientists are fixated with their publications to make a steady income, they must push whatever will allow the safest income. Even though going with the more innovative idea may result in a breakthrough that will net massive amounts of revenue from publication, there is an even greater chance that the study will not result in a positive study, which would not be beneficial to the scientist. This risk versus reward scenario causes scientists to then make a choice on what they value more. There, the non-innovative route becomes the favored choice as scientist do not have a safety net that can warrant the risk. Thus, innovation is slowly starting to slow down. This is one of the worst outcomes as only innovation causes new leaps and bounds to be made from science. If innovation is starting to slow down, science as a whole slows down as well.
Since all of these issues can be solved by money, funding from organizations seem to be one of the best solutions. Money is being given to the researchers which allows the researchers to remove the restraint of income so better tests are made. However, this harmonious relationship becomes detrimental as both parties benefit too much. A claim from a scientific study is very valuable for a business. The faith people have with how rigid scientific studies are causes people to believe essentially anything a scientific study proves. As a result, companies are willing to invest a lot of money for scientific studies that positively help whatever the company is pushing. This investment would ultimately result in more money for the future. This interest itself causes a cycle that makes this issue worse. A business wants to be able to push their values to gain more money or popularity, so the businesses are more willing to pay money to inevitably reap the benefits. As the business itself pays money for the studies that prove their values, scientists are more enticed to make a study that proves the business’s value for a better living, giving more and more incentive to produce more or alter claims that prove the value.
This cycle results in countless biased articles that unjustifiably prove the claim of the business that affect the public. Companies such as pharmaceuticals and sport drink companies are repeatedly found in the obvious malpractice. For example, in the study “Association of Funding And Conclusions in Randomized Drug Trials,” Bodil ALs-Nielsen randomly selected 370 random drug trials to see if there was an effect on the result of the test being funded by a non-profit organization or a for profit organization. With only 16% of the studies recommending the drugs when it was funded by a non-profit organization and 51% of the studies when funded by a for-profit organization, it is painfully obvious to see the effect that funding sources has.
Works Cited:
Turner, Erick H. “Selective Publication of Antidepressant Trials and Its Influence on Apparent Efficacy — NEJM.” New England Journal of Medicine. N.p., 17 Jan. 2008. Web. 28 Nov. 2016.
Hampton, Phil. “Pressure to ‘publish or Perish’ May Discourage Innovative Research, UCLA Study Suggests.” UCLA Newsroom. N.p., 08 Oct. 2015. Web. 018 Nov. 2016
Nielsen, MD Bodil. “Association of Funding and Conclusions in Randomized Drug Trials.”Association of Funding and Conclusions in Randomized Drug Trials. The JAMA Network, 20 Aug. 2003. Web. 01 Dec. 2016.
The Truth Can Be Skewed
Scientific studies allow science to expand its knowledge, from finding relationships for two seemingly different entities to testing and explaining phenomena that the world doesn’t quite understand. With correct use of scientific studies scientist can achieve feats that would not have been deemed possible without the newly found knowledge. More cures can be found, larger realizations and trends can be identified, and even more knowledge of a field can potentially make grows as even more studies can elaborate. However, the massive influence that scientific studies is a double-edged sword. With the massive influence studies have, these studies can determine what is the truth. However, studies are still fallible and studies that push false claims can skew what the common people believe is the truth.
As expected, scientific studies have a very rigid system that permits what studies have to accomplish to make a claim. In order for a scientific study to prove a claim, scientifically known as a hypothesis, the study must prove that the hypothesis must have an undeniable relationship. In order to prove the hypothesis, the scientists then form what is known as a null hypothesis, which assumes the that there is no correlation between the two. For example, if the hypothesis is that a newly made drug increases dopamine levels, the null hypothesis would be that the drug did not exhibit any change in dopamine levels. The scientists then attempt to prove the actual hypothesis by rejecting the null hypothesis.
The data, which is found by the carefully thought-out tests and conditions that researchers place, is then analyzed to see if the data was statistically significant to see whether the scientists can or cannot reject the null hypothesis. This test to see if something is statistically significant is essentially finding whether the data was gotten due to random chance or if the claim is the reason behind the data. The researchers then use many different methods to calculate the probability of how likely the data that was given could have shown up, also known as the p-value. To say something was statistically significant, the probability must be lower than 5 percent. This magic number of 5 percent is the key part or the bane of scientists, as any study that produces a p value lower than 5 percent is determined to be validated as the probability of the null hypothesis being true statistically improbable and rejected, which therefore makes the actual hypothesis true. Any p-value that is 5 percent or higher cannot reject the null hypothesis and cannot prove the claim that the study was trying to make which causes the scientist to either reattempt the study or change the claim altogether.
This system is not a perfect system by any means. Natural errors can still occur when validating the claim. As the data still have a factor of chance in them, some errors can occur without any influence from the scientists. These errors are known as Type I error and Type II error. A Type I error occurs when you reject the null hypothesis and say that the claim was true even though it was false. For example, a type I error would be stating that someone had a disease even though the person does not have the disease. A type II error is the exact opposite, where you reject the null hypothesis and make the actual claim false even though it was true. For example, in the same scenario a type II error would state that someone did not have a disease even though it the person did have it. Both errors are bad, but these errors are accounted for by scientists. However, the issue comes when scientists intentionally publish what is supposed to be a type I error.
Intentional errors become a major issue as the scientific studies that people take at face value flood the scientific journals become either misleading or entirely untrue. Studies that affect the percentage of c undergo effects such as publication bias and the file-drawer effect describe parts The author Megan L. Head, in the article “The Extent and Consequences of P-Hacking in Science,” defines publication bias as, “the phenomenon in which studies with positive results are more likely to be published than studies with negative results.” The file-drawer effect is the tendency for scientists to refrain from publishing negative studies as due to the lack of money. These effects are very detrimental as there is a noticeable underrepresentation of negative published studies. In “The file drawer problem and tolerance for null results,” Robert Rosenthal describes this effect by saying, “the extreme view of the ‘file drawer problem’ is that journals are filled with the 5% of the studies that show Type I errors, while the file drawers are filled with the 95% of the studies that show nonsignificant results.” This is direct result of scientist attempting to push studies that innately get more attention, as positive-resulting, intriguing studies will be more popular than negative-resulting studies.
However, the bias can even be more direct with something known as p-hacking. Through p-hacking, Scientist can attempt to alter the way they compute the p-value with any given data, since the essential part of a study is primarily based on the comparison of the p-value, in order to find something that is statistically probable. In the web article “Is Science Broken?” author Christie Aschwanden simulated how easy it is to find something is statistically significant for many different hypothesis with the same data. In his simulation, we are given to choose a category on which political party, Republican or Democratic, we want the hypothesis to support. Aschwanden then demonstrated that, by choosing what choosing to keep and omit some parts of the data such as the type of politicians that we want to consider as politicians and including recessions, the combination of different parts of the data can prove hypothesis for both sides. Even with the same data, the fact that the use of p-hacking can prove completely opposite ideologies shows the massive influence that p-hacking can have.
Works Cited:
Head, M. L. “The Extent and Consequences of P-Hacking in Science.” The Extent and Consequences of P-Hacking in Science. PLoS Biol, n.d. Web. 18 Nov. 2016.
Aschwanden, Christie. “Science Isn’t Broken.” FiveThirtyEight. N.p., 19 Aug. 2016. Web. 15 Nov. 2016.
Rosenthal, Robert. “The File Drawer Problem And Tolerance For Null Results.” Psychological Bulletin 86.3 (1979): 638-641. PsycARTICLES. Web. 15 Nov. 2016.
The FDA Does Its’ Best
In order to make sure that harmful products do not go the people, organizations such as the FDA-also known as the Food and Drug Administration-place very rigid requirements. However, regulatory associations such as the FDA are simply not enough to keep the influence of drug companies away from scientific studies.
Petter Hutt’s paper, “Untangling the Vioxx-Celebrex Controversy: A Story about Responsibility.” on the controversy describes the exact context on how the FDA approves a drug. The FDA first requires what is known as an NDA or new drug application. The new drug then undergoes the Investigation New Drug, or IND, and three Phases. The IND test to see if the production and analyzation had, “protection of the human research project, animal studies completed and analyzed, scientific merit, and qualifications of the investigator.” From the IND, the drug then undergoes Phase I, II, and III. Phase I tests the drug on one subject to check for adverse side effects, which moves on to phase II if successful. Phase II administers the drug multiple times on a small group,, which will move on to phase III. In this phase the drug is given to thousands of patients with many different methodologies in order to check for drug interactions/reactions. It is estimated that this entire process takes around 7 to 13 years before the application is finished. After the application is submitted, the FDA then makes a committee to push the new drug and either authorize the drug or stop the process there.
This very methodical authorization system should be able to handle drug after numerous checks. However,the unreliability of the FDA is completely exposed with the Vioxx controversy. DrugWatch, in the web article “Vioxx Recall – Merck and FDA,” discusses the painkiller Vioxx and how it was spread to many different doctors with the primary goal of giving the drug to as many patients as possible. However, in only 5 years ,this seemingly harmless drug was found to more the double the risk of heart attacks and death. Eventually, in 2004 Merck recalled Vioxx after being put in the spotlight for their drug. DrugWatch described the havoc Vioxx caused, with over 38,000 deaths, as potentially, “ the worst drug disaster in history”
The drug went through the entire rigid appeal process of the FDA and was approved in 1999. Not once did the FDA stop the drug until the symptoms the heart issues started to appear and an anylazation was made. But by the time the FDA caught on, Vioxx already damaged thousands of lives. The reason why this disaster even occurred was due manipulating data. In order for the Merck scientists to show that the drug was safe enough for use, they omitted the detrimental data pertaining to patients with heart complications or else the drug couldn’t have been released. In fact Hutt stated that, “the General Accounting Office found that of 198 drugs approved by the FDA between 1976-1985, about half had serious post-approval problems.”
Not only that, but this controversy also shed light on corruption of the FDA. It was noted that Merck persuaded the FDA to remove warning labels for digestive issue with Vioxx even before the drug was approved. The FDA also ignored the doctors claims of patients’ hearts problem until in 2002 a study that showed the relationship between heart complications and Vioxx. When that integral piece of information came out, all the FDA did was simply add a label.
The FDA had numerous times to prevent a disaster from happening and the organization was built to do just that. However, the bias that Merck was push forward to push their product slipped through, which means shows even the FDA struggles to mitigate the effect of bias in scientific studies.
Works Cited:
Hutt, Peter Barton. “Untangling the Vioxx-Celebrex Controversy: A Story about Responsibility.”Tran, Lan. N.p., 4 May 2005. Web. 18 Nov. 2016.
“Vioxx Recall – Merck and FDA.” DrugWatch. N.p., n.d. Web. 18 Nov. 2016.
Esports in The Public
What really is holding back competitive gaming from becoming part of the mainstream, is the public. Accepting up and coming new trends can be difficult for the people who are accustom to the already established norm of their culture. Media outlets like CBS, HBO and TMZ in the past all have incorrectly defined the sport and have also given it a image that they believe fits their agendas. TMZ goes to the extant of writing “Question — What do pimply-faced geeks who play video games all day have in common with the 6’8″ demigods who roam NBA courts on a nightly basis???” In the description portions of one of their YouTube videos. In this video they interview Rick Fox owner of Echo Fox (eSport Franchise) and also former NBA star for the Boston Celtics and LA Lakers.
Now its fair to assume that TMZ learned their lesson after the backlash they received from the eSport community for writing something like that. Well in less than a month TMZ does another interview with Rick Fox and instead of fixing their mistakes they just repeat the cycle. TMZ goes onto writing “Beware NHL fans … Rick Fox says your sport will be overtaken … by nerds … ’cause the “League of Legends” team owner thinks eSports is primed to take hockey’s place as the fourth major sport in the U.S. in just TWO YEARS!”. This constant cycle of misjudging the eSport community just doesn’t end even with the professional world of news/media.
For a news/media outlet to set specific physical standards for sports players just goes to show how ignorant some can be. Can this trend end? will the image that society gives video game playing ever change? If our media cannot accept this new outlet then the casual viewer will have no desire to follow eSports or learn about it. How did our culture come to know and love the sport of basketball or football? Well soon after the creation of these sports back in the late 1800’s, the creation of leagues rose and opened up the gates to the world of professional sports. with their own rules and regulations. They could turn a fun game to play and pass time, to something that can be considered a career, grant fame, fortune and set a standard of living that is now portrayed as the perfect life to regular society.
Esports just like professional sports started with its creation of leagues. Leagues that go by the name of Electronic Sports League (ESL) and League of Legends Championship Series (LCS). These are some of the larger brand leagues that come along with their own rules and regulations, Which is no different from from the NBA or the NFL What makes basketball and football so popular in North America is its ability to create rivalries between fans and players stemming off of the fact that each team represents a state or city. Which then causes an increase in enthusiasm and support for teams. Esports on the other hand is on the international level of fan bases. Where organisations can represent multiple countries rather than just national states.
Community and fan base is everything when it comes to sports along with professionalism and extreme consistency in the skill of said athletes. But if an entire culture is going to misinterpret the underlying definition of what a sport really is and what it adds to society then without a doubt mindsets will not change and this sport will belong in the niche category of sports.
Works cited
TMZSports. “Rick Fox- ESports Jocks Are Just Like NBA Players…Real Athletes | TMZ Sports.” YouTube. YouTube, 12 Mar. 2016. Web. 02 Dec. 2016.
TMZSports. “Rick Fox- ESports Will Overtake NHL In 2 Years!! | TMZ Sports.” YouTube. YouTube, 21 Mar. 2016. Web. 02 Dec. 2016.
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Background: This article explains the relationship between parents and their children. This article goes on to explain how bad habits are formed and continued. This article goes on to show that the media attacks children more than adults which ultimately affect the adults in the long run.
How I used it: I used this article to show the parent to child relationship. This relationship is one of the most difficult to understand as every relationship is different. This article is a scholarly source I retrieved from google scholar that helped me better comprehend other information I received from other sources.
2. Lobstein, Tim, and Rachel Jackson-Leach. “Child overweight and obesity in the USA: prevalence rates according to IOTF definitions.” International Journal of Pediatric Obesity 2.1 (2007): 62-64.
Background: This article talks about obesity found in children in the USA. This source helps strengthen my argument as it supports my argument that obesity is a big problem we face in America today.
How I used it: I used this article to strengthen my own. Childhood obesity is a problem that could be easily resolved with the help of their parents. Without proper guidance we continue to fall into the same consistent bad eating habits which end up getting brought down onto their children.
3. Locard, Elisabeth, et al. “Risk factors of obesity in a five year old population. Parental versus environmental factors.” International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity 16.10 (1992): 721-729.
Background: This article goes into depth talking about risk factors of obesity in a five year old population. This article shows how important prevention of obesity is and that obesity is affected by many different other environmental factors.
How I used it: I utilized this article to show the importance and how easy it is to prevent obesity in children just by monitoring what they watch on television. Using this and other prevention methods childhood obesity can be prevented and obesity could ultimately be lowered.
4. Puhl, Rebecca M., and Chelsea A. Heuer. “The stigma of obesity: a review and update.” Obesity 17.5 (2009): 941-964.
Background: The article goes into depth talking about the stigma that surrounds obese America. This article talks about the many different types of stereotypes that also goes on to talk about bias and discrimination that people face in more places than just the workplace.
How I utilized it: I used this source to get an in depth side of view of those who feel they are victimized by the disease. How this affects their every day living and how they learn to live and cope with it. This source gives me the availability to utilize their first person knowledge to utilize into my argument.
5. Wang, Youfa, et al. “Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic.” Obesity 16.10 (2008): 2323-2330.
Background: A source that projects the results from the National Health and Nutrition Examination study which began in the 1970’s and concluded in 2004. Very dependable and a good scholarly source.
How I used it: I’m going to use their results to support my argument. Their study goes into depth about how big obesity is becoming in the United States.
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Background: This source gave statistics regarding obesity. Gave me more factual evidence.
How I used it: I used their data and noted it in my paper.
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Background: A very well written article by a credible author in The Obesity Society. Gave good preventive measures to fighting obesity.
How I used it: I used their statistics and opinion. I appreciated and noted their preventive solutions to obesity.
8. Reilly John J, Armstrong Julie Dorosty, Ahmad R, Emmett Pauline M, Ness A,Rogers I et al. “Early life risk factors for obesity in childhood: cohort study” BMJ 2005; 330: 1357.
Background: A credible source that addresses early life risk facts for obesity in childhood and adolescence.
How I used it: This was a source I may or may not use in my final research paper.
9. Laroche, Helena. “Concession Stand Makeovers: A Pilot Study of Offering Healthy Foods at High School Concession Stands.” Food and Brand Lab |. N.p., n.d. Web. 31 Oct. 2016.
Background: Just a popular source I found which talks about statistics which also offers an alternative attack on obesity.
How I used it: Offering an alternative approach to the ongoing battle on obesity.
10. Hyppönen, Elina, et al. “Obesity, increased linear growth, and risk of type 1 diabetes in children.” Diabetes care 23.12 (2000): 1755-1760.
Background: A scholarly source which explains a recent study in the very rapid increase of type 1 diabetes found in children.
How I used it: I used this to explain the increased growth of obesity found in children. Going along with preventive measures.