Help me with a Science assignment
- Thread starter TheHamburglar
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Sep 11, 2015 at 9:17 AM
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WhyCan'tWeHaveBoth.gif
(BTW PC=Personalized Computer, which Mac and Windows both are)
(BTW PC=Personalized Computer, which Mac and Windows both are)
Sep 11, 2015 at 12:59 PM
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Despite what the acronym 'PC' stands for, it has been very strongly attached to Windows Computers.
Sort of like the how the terms 'google' and 'photoshop' mean other things too. (Okay, this is a minor stretch though, but you should get what I mean)
Sep 12, 2015 at 1:51 AM
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I kinda like GNU/Linux. I hate Microsoft but I use Win7. I view Apple as a big, successful brainwashing sect.
But who cares. I'd say this thread should be more about the scientific method than about PC vs Mac, so I'll elaborate on that instead of dabbling in pointless arguments between the Windows hoes, the Apple sheeple and the Debian bandwagon.
If you have read the spoiler above, then you may have noticed that the scientific method does not seem to cater to the subject of this thread.
Indeed, this is a case where the question is not about an observational event. The goal is not to advance scientific knowledge but to know people's preferences for marketing or political purposes. It is not a scientific process at its core.
Since there is no observational event involved in the question, there cannot be a hypothesis. If there is no hypothesis, there is no study with which you can obtain empirical data. Instead, the data is non-empirical and comes from an opinion poll.
Nonetheless, the scientific rules can and should still be applied to the polling, and the gathered data can still be analyzed in a scientific fashion.
In my opinion, the scientific method itself is good, but not transcendental. It is merely a consequence of its elementary foundations, which can be applied to the process of opinion polls: rigorousness, objectivity, and the acknowledgement that absolutely nothing is set in stone.
Indeed, it is extremely hard (basically impossible, unless we're talking about exact sciences like mathematics, AND EVEN THEN
), I say, it is impossible to obtain results that are undoubtedly the inalterable truth. For example, when talking about opinion polls, the problems are that
- the Earth is very big,
- people are all different,
- people change,
- and people lie.
Let's use the example of this thread: "do people like Mac or PC more?". A simple question.
But you can't ask this question to everybody in the world, because of logistics: some people don't have the internet and you can't really meet everyone in the world to ask them the question (and also, because of knowledge levels: a lot of people don't know what PCs and Macs are).
If you don't ask everybody (who knows what you're talking about), then since everybody is different, there is a chance that you asked this question to people who were generally more on the PC side of things, for some reason. Here, there is an obvious possible reason: Cave Story was first released on PC, and consequently, there are probably more people on these Cave Story forums who are PC users.
But even if you somehow did ask everybody, then the time it took for you to ask half the planet might have been enough for the other half of the planet to change their opinion.
And even if you somehow asked everybody at the same time, maybe half of the people who answered lied to you because they are ashamed of what they like and want to make it look like they agree with what they think is the majority.
So in the end, the answer you reach still cannot be taken for truth, no matter how scientific your method was and even if you were completely rigorous.
And on the other hand, it is extremely easy to NOT be rigorous, and if you are not, then you can ipso facto throw your results in the trashcan.
Finally, the best way to increase truthfulness of your results is to subject them to peer reviews: a community of experts in the field impartially reviewing your study, its results, its analysis and its conclusions, in order to decide of its quality, rigorousness and objectivity, and whether it should be accepted, revised or rejected for publication.
The reviewers and their opinion can themselves be doubted, as impartiality is hard to reach. It helps, though.
In conclusion: whenever you find "groundbreaking new studies" about touchy subjects on the internet, there is a 99% chance that the results of the study are either inaccurate, wrong, falsified, greatly exaggerated, not significant, or taken out of context ("out of the 5 white male american vegetarian people who were asked, 3 said they were lactose intolerant, INCREDIBLE EVOLUTIONARY OUTBREAK! HUMANITY SOON WILL NOT BE ABLE TO DRINK MILK ANYMORE" published by WeLoveVeganism). The remaining 1% are results showing correlation between two events that is probably a total coincidence but YOU NEVER KNOW.
Science is a bitch. Still the best tool we have out there. Cherish it.
But who cares. I'd say this thread should be more about the scientific method than about PC vs Mac, so I'll elaborate on that instead of dabbling in pointless arguments between the Windows hoes, the Apple sheeple and the Debian bandwagon.
The scientific method is pretty straightforward.
1. Find a subject
2. Ask a question about an observable event in this subject
3. Formulate a theory and propose a hypothesis that aims to explain the observed event
4. Conduct a study to obtain results about the hypothesis
5. Draw conclusions to confirm or infirm the hypothesis
I'll cover those in turn:
1. Find a subject
2. Ask a question about an observable event in this subject
3. Formulate a theory and propose a hypothesis that aims to explain the observed event
4. Conduct a study to obtain results about the hypothesis
5. Draw conclusions to confirm or infirm the hypothesis
I'll cover those in turn:
1. Of course, the first thing to do when you're using the scientific method is to find a subject to use it on. But you also have to get the whole context behind it, and make sure you can find an answer.
Find a field of work, and study it in order to achieve state-of-the-art knowledge (it doesn't mean you know everything about the field, but rather that you know what people do or do not know in the field). Of course, usually it's the field you're already specializing in, so you would already know quite a bit about it.
Example: vaccinology.
Find a field of work, and study it in order to achieve state-of-the-art knowledge (it doesn't mean you know everything about the field, but rather that you know what people do or do not know in the field). Of course, usually it's the field you're already specializing in, so you would already know quite a bit about it.
Example: vaccinology.
2. Find a question in the chosen field that you know is still unanswered, or a question whose answer is not convincing you.
-> In the former case, you'll have to gather all existing information and definitions relative to the elements of the question.
-> In the latter case, the aforementioned info gathering job is easier, but you'll also have to gather all existing information relative to the current accepted answer(s).
Example: "do vaccines cause autism?" (this question has been answered a million times already but let's go with it).
Then, check that your question is answerable, that is, if it is actually possible to find and formulate an unambiguous answer to it. For that, there are some things you have to make sure of:
- All of the terms used in the question can be objectively and unambiguously defined ("What is the best OS?" is a bad question, because "best" is a subjective notion).
- Your question is centered on an observable event ("Is there a god?" is a bad question, because there is no way to observe such a thing as the existence of a god).
Example: all terms can be defined. The observable event is a possible correlation between "vaccines" and "autism". But these two terms should be more precise.
- "Vaccines" might be a bit too large: let's settle for one of them, like the Flulaval vaccine for influenza. We can always study different vaccines separately.
- We could also choose only one disorder on the spectrum of "autism", but since we do not have to gather new data for each of them, it doesn't hurt to focus on all of them at the same time (actual autism, Asperger syndrome and PDD-NOS).
Then, make sure your question is accurately targeted at your demographic of interest, that is, the question you ask must be precise enough to contain every element you wish to account for in your observations. Otherwise your results will be off-context and will not answer the question.
Example: in our case, the question is implicitly aimed at people who are neither vaccinated nor autistic. We could choose to reduce it further by adding an age group, like "children 4 to 8 years old".
The question is now: "Does the Flulaval vaccine cause any of the three kinds of autism in children aged 4 to 8 years old?"
-> In the former case, you'll have to gather all existing information and definitions relative to the elements of the question.
-> In the latter case, the aforementioned info gathering job is easier, but you'll also have to gather all existing information relative to the current accepted answer(s).
Example: "do vaccines cause autism?" (this question has been answered a million times already but let's go with it).
Then, check that your question is answerable, that is, if it is actually possible to find and formulate an unambiguous answer to it. For that, there are some things you have to make sure of:
- All of the terms used in the question can be objectively and unambiguously defined ("What is the best OS?" is a bad question, because "best" is a subjective notion).
- Your question is centered on an observable event ("Is there a god?" is a bad question, because there is no way to observe such a thing as the existence of a god).
Example: all terms can be defined. The observable event is a possible correlation between "vaccines" and "autism". But these two terms should be more precise.
- "Vaccines" might be a bit too large: let's settle for one of them, like the Flulaval vaccine for influenza. We can always study different vaccines separately.
- We could also choose only one disorder on the spectrum of "autism", but since we do not have to gather new data for each of them, it doesn't hurt to focus on all of them at the same time (actual autism, Asperger syndrome and PDD-NOS).
Then, make sure your question is accurately targeted at your demographic of interest, that is, the question you ask must be precise enough to contain every element you wish to account for in your observations. Otherwise your results will be off-context and will not answer the question.
Example: in our case, the question is implicitly aimed at people who are neither vaccinated nor autistic. We could choose to reduce it further by adding an age group, like "children 4 to 8 years old".
The question is now: "Does the Flulaval vaccine cause any of the three kinds of autism in children aged 4 to 8 years old?"
3. After you have found a question to ask yourself, you have to formulate a hypothesis, which is NOT just a proposed answer to the question. It is actually a proposed explanation for the observed event taking place in the question.
A hypothesis has to be testable, and will usually suggest a causal relationship between two events. In this case, it contains at least one dependant variable and one independant variable.
- The dependant variable is a value that supposedly depends on the independant variable. It's the value you're observing.
- The independant variable is a value manipulated by the researcher in order to see if the dependant variable(s) change.
Example: our theory can be "The Flulaval vaccine increases the chances of developing one of the three kinds of autism in children aged 4 to 8 years old."
From there, our hypothesis could be something like "The preservative called Thimerosal in Flulaval vaccines contains mercury, which impairs neurological development in infants and children". Neat.
A hypothesis has to be testable, and will usually suggest a causal relationship between two events. In this case, it contains at least one dependant variable and one independant variable.
- The dependant variable is a value that supposedly depends on the independant variable. It's the value you're observing.
- The independant variable is a value manipulated by the researcher in order to see if the dependant variable(s) change.
Example: our theory can be "The Flulaval vaccine increases the chances of developing one of the three kinds of autism in children aged 4 to 8 years old."
From there, our hypothesis could be something like "The preservative called Thimerosal in Flulaval vaccines contains mercury, which impairs neurological development in infants and children". Neat.
4. After you have formulated a theory and a hypothesis, you need empirical data in order to provide a meaningful answer. To gather that data, you have to conduct an experiment (if you have access to the independant variable) or an observational study (if you don't).
To do that, you have to design a protocol that allows you to gather the data in the most accurate way possible. The protocol must describe a procedure that allows you to manipulate the independant variable(s) and observe the results on the dependant variable(s) while preventing any influences from other, unrelated variables.
You also need a certain amount of people from your demographic target that are willing to be made subjects of your experiment. You need enough of them for the data to be significant.
Example: in our case, the independant variable would be the amount of Thimerosal in Flulaval vaccines, and the dependant variable would be the amount of autisms in the population.
Obviously, the ideal way to deal with this would be to change the amount of Thimerosal in the vaccines administered during the experiment, and checking if it affects the amount of autisms developing among the test subjects in the next years.
However, we're running into an issue there: administering fake/tweaked vaccines for the purpose of an experiment is probably not considered ethical. I wonder why, geez.
Anyway, this essentially means we do not have access to the independant variable, so conducting an experiment is not possible. Instead, we will have to resort to an observational study. Basically, it involves observing naturally-occuring occurences of the events (that is, actual vaccines on normal people), and comparing the results with people who were not vaccinated. Less accurate (as people who were not vaccinated are not subjects to the effects of other components in the vaccines, which are unrelated variables and should not change between subjects), but we'll have to deal with it.
N.B.: The most rigorous way to conduct an experiment is probably to make it double-blind, but I will talk more about rigorousness in studies later in this post.
To do that, you have to design a protocol that allows you to gather the data in the most accurate way possible. The protocol must describe a procedure that allows you to manipulate the independant variable(s) and observe the results on the dependant variable(s) while preventing any influences from other, unrelated variables.
You also need a certain amount of people from your demographic target that are willing to be made subjects of your experiment. You need enough of them for the data to be significant.
Example: in our case, the independant variable would be the amount of Thimerosal in Flulaval vaccines, and the dependant variable would be the amount of autisms in the population.
Obviously, the ideal way to deal with this would be to change the amount of Thimerosal in the vaccines administered during the experiment, and checking if it affects the amount of autisms developing among the test subjects in the next years.
However, we're running into an issue there: administering fake/tweaked vaccines for the purpose of an experiment is probably not considered ethical. I wonder why, geez.
Anyway, this essentially means we do not have access to the independant variable, so conducting an experiment is not possible. Instead, we will have to resort to an observational study. Basically, it involves observing naturally-occuring occurences of the events (that is, actual vaccines on normal people), and comparing the results with people who were not vaccinated. Less accurate (as people who were not vaccinated are not subjects to the effects of other components in the vaccines, which are unrelated variables and should not change between subjects), but we'll have to deal with it.
N.B.: The most rigorous way to conduct an experiment is probably to make it double-blind, but I will talk more about rigorousness in studies later in this post.
5. Once you have gathered empirical data, you can analyze it in order to draw conclusions and provide an answer to your question.
But there's a twist! No matter the amount of evidence, you cannot actually prove anything. Your goal is to either observe correlation between the independant variable(s) and the dependant variable(s) (which would NOT prove your hypothesis, only support it), or not observe any (which would NOT disprove your hypothesis, it would only mean the correlation is that much less likely to exist).
Example: by comparing the proportion of autisms in vaccinated and unvaccinated children aged 4 to 8 years old, we observe no correlation between the two variables. We conclude that the Flaluval vaccine probably does not cause any kind of autism in this demographic.
But there's a twist! No matter the amount of evidence, you cannot actually prove anything. Your goal is to either observe correlation between the independant variable(s) and the dependant variable(s) (which would NOT prove your hypothesis, only support it), or not observe any (which would NOT disprove your hypothesis, it would only mean the correlation is that much less likely to exist).
Example: by comparing the proportion of autisms in vaccinated and unvaccinated children aged 4 to 8 years old, we observe no correlation between the two variables. We conclude that the Flaluval vaccine probably does not cause any kind of autism in this demographic.
If you have read the spoiler above, then you may have noticed that the scientific method does not seem to cater to the subject of this thread.
Indeed, this is a case where the question is not about an observational event. The goal is not to advance scientific knowledge but to know people's preferences for marketing or political purposes. It is not a scientific process at its core.
Since there is no observational event involved in the question, there cannot be a hypothesis. If there is no hypothesis, there is no study with which you can obtain empirical data. Instead, the data is non-empirical and comes from an opinion poll.
Nonetheless, the scientific rules can and should still be applied to the polling, and the gathered data can still be analyzed in a scientific fashion.
In my opinion, the scientific method itself is good, but not transcendental. It is merely a consequence of its elementary foundations, which can be applied to the process of opinion polls: rigorousness, objectivity, and the acknowledgement that absolutely nothing is set in stone.
Indeed, it is extremely hard (basically impossible, unless we're talking about exact sciences like mathematics, AND EVEN THEN
you cannot actually prove that any mathematics are right, because all of it relies upon sets of elementary truths called axioms, like "things can equal other things", "if two lines are parallel to a third line then they are parallel to each other", and "0 is a number", which cannot be proven without having to resort to the other axioms.
Moreover, Kurt Gödel has proven that for every possible set of axioms, there are propositions considered true that cannot be proven from the chosen axioms.
What this means is that you will never be able to perfectly demonstrate any mathematical theorem because they rely upon other theorems which rely upon axioms which cannot be proven true and cannot prove everything else is true. Suck on that, Hilbert.
Moreover, Kurt Gödel has proven that for every possible set of axioms, there are propositions considered true that cannot be proven from the chosen axioms.
What this means is that you will never be able to perfectly demonstrate any mathematical theorem because they rely upon other theorems which rely upon axioms which cannot be proven true and cannot prove everything else is true. Suck on that, Hilbert.
- the Earth is very big,
- people are all different,
- people change,
- and people lie.
Let's use the example of this thread: "do people like Mac or PC more?". A simple question.
But you can't ask this question to everybody in the world, because of logistics: some people don't have the internet and you can't really meet everyone in the world to ask them the question (and also, because of knowledge levels: a lot of people don't know what PCs and Macs are).
If you don't ask everybody (who knows what you're talking about), then since everybody is different, there is a chance that you asked this question to people who were generally more on the PC side of things, for some reason. Here, there is an obvious possible reason: Cave Story was first released on PC, and consequently, there are probably more people on these Cave Story forums who are PC users.
But even if you somehow did ask everybody, then the time it took for you to ask half the planet might have been enough for the other half of the planet to change their opinion.
And even if you somehow asked everybody at the same time, maybe half of the people who answered lied to you because they are ashamed of what they like and want to make it look like they agree with what they think is the majority.
So in the end, the answer you reach still cannot be taken for truth, no matter how scientific your method was and even if you were completely rigorous.
And on the other hand, it is extremely easy to NOT be rigorous, and if you are not, then you can ipso facto throw your results in the trashcan.
What does it mean to be rigorous? It means to never let luck nor subjectivity interfere with your results when you can avoid it.
I talked about how asking these forums "Mac or PC?" could be a bad idea as people on these forums may be biased towards PC; this is exactly what it's about. You have to make sure the set of people you asked the question come from different backgrounds and do not have anything in common, except what is included in the question. For example:
- A. "Do people prefer Mac or PC?" means you should ask, in parts that are proportionate to reality, white, black, asian people. Old people, young people, middle-aged people. Men, women. Gay, bi, heterosexual people. Workers, employees, CEOs, jobless people, university, high school, middle school, engineering school students, people from this site, from elsewhere, people without internet. Should you ask people who do not have computers? Probably, since nothing in your question says otherwise, which echos my earlier point about formulating a precise question. You have to ask the question to as many people you can, coming from all backgrounds, because your questions is aiming at the entire population of Earth by default, so you cannot favor one category over others.
- B. "Do active CSTSF members prefer Mac or PC?" is an easier question, since you are restraining it to a certain category of people. Although, you should still be careful about the different categories of people; if the CSTSF is 75% males, but 80% of the answers you get are from female members, then the result is wrong since it does not represent well the demography your question is targeting.
This is why, in polls, there are questions about gender, age, sometimes income class, etc. It's because you have to be careful that the people who actually answered form a proportionate fraction of the target demography.
But this question has something more to it: implicitly, you have to compare active CSTSF members to the norm. So you would still need to obtain an answer to question A ("Do people prefer Mac or PC?") and then compare both questions' results! If they are the same, then you can conclude that "active CSTSF members do not have stronger preferences than the average when it comes to their OS of choice". If there are more PC enthusiasts among the second result, then you can conclude that "active members of the CSTSF like PC more than the average".
Now, the next step is making sure answers are as truthful as possible. You cannot avoid people answering randomly to your poll, but you can avoid people trying to cheat the system by answering in a way that is as (dis)satisfactory as possible for the poller.
To do this, you have to avoid giving any hint as to what your study is about. If your question is something like "Are jobless people lazier than average", and your poll is really obvious about it, then jobless people will probably be more inclined (consciously OR subconsciously, which is the biggest problem here) to answer in a way that caters to their pride, and you will end up with skewed results of jobless people being ultra-motivated.
And that goes for you too! If you like PC more, then (again, consciously OR subconsciously) you might desire the results to be in favor of PC, and ask the questions in such a way that it will incite people to answer in the way you want.
Example:
"Which OS do you prefer?
- Windows 7
- Windows XP
- Windows 10
- Other Windows systems
- Mac OS X
- Other Mac systems
- GNU/Linux".
See what I mean? If you, the researcher, like Windows more, you might allow people to check several options and then add together all the votes for Windows systems. Since there are four of them, and Windows fanboys will probably have checked all four of them, then you give a humongous advantage to Windows.
But if you like Apple more, then you might disallow checking several options and then take the average of each system of a particular brand. Since Windows fanboys will be spread between more options, then the average of each option will be lower than it should, which will greatly disadvantage Windows over the others. You swine.
Moreover, when the time comes to analyze the data you gathered, you might tweak the results (to account for categories of people) in a way that makes one option win. Which is why you have to take measures to automatize the process, or get it done by a neutral third party (that doesn't know and cannot figure out from the data what the poll is about). It might be a good idea to throw random questions about other stuff in the poll, in order to hide which questions are relevant to the subject of the study.
I talked about how asking these forums "Mac or PC?" could be a bad idea as people on these forums may be biased towards PC; this is exactly what it's about. You have to make sure the set of people you asked the question come from different backgrounds and do not have anything in common, except what is included in the question. For example:
- A. "Do people prefer Mac or PC?" means you should ask, in parts that are proportionate to reality, white, black, asian people. Old people, young people, middle-aged people. Men, women. Gay, bi, heterosexual people. Workers, employees, CEOs, jobless people, university, high school, middle school, engineering school students, people from this site, from elsewhere, people without internet. Should you ask people who do not have computers? Probably, since nothing in your question says otherwise, which echos my earlier point about formulating a precise question. You have to ask the question to as many people you can, coming from all backgrounds, because your questions is aiming at the entire population of Earth by default, so you cannot favor one category over others.
- B. "Do active CSTSF members prefer Mac or PC?" is an easier question, since you are restraining it to a certain category of people. Although, you should still be careful about the different categories of people; if the CSTSF is 75% males, but 80% of the answers you get are from female members, then the result is wrong since it does not represent well the demography your question is targeting.
This is why, in polls, there are questions about gender, age, sometimes income class, etc. It's because you have to be careful that the people who actually answered form a proportionate fraction of the target demography.
But this question has something more to it: implicitly, you have to compare active CSTSF members to the norm. So you would still need to obtain an answer to question A ("Do people prefer Mac or PC?") and then compare both questions' results! If they are the same, then you can conclude that "active CSTSF members do not have stronger preferences than the average when it comes to their OS of choice". If there are more PC enthusiasts among the second result, then you can conclude that "active members of the CSTSF like PC more than the average".
Now, the next step is making sure answers are as truthful as possible. You cannot avoid people answering randomly to your poll, but you can avoid people trying to cheat the system by answering in a way that is as (dis)satisfactory as possible for the poller.
To do this, you have to avoid giving any hint as to what your study is about. If your question is something like "Are jobless people lazier than average", and your poll is really obvious about it, then jobless people will probably be more inclined (consciously OR subconsciously, which is the biggest problem here) to answer in a way that caters to their pride, and you will end up with skewed results of jobless people being ultra-motivated.
And that goes for you too! If you like PC more, then (again, consciously OR subconsciously) you might desire the results to be in favor of PC, and ask the questions in such a way that it will incite people to answer in the way you want.
Example:
"Which OS do you prefer?
- Windows 7
- Windows XP
- Windows 10
- Other Windows systems
- Mac OS X
- Other Mac systems
- GNU/Linux".
See what I mean? If you, the researcher, like Windows more, you might allow people to check several options and then add together all the votes for Windows systems. Since there are four of them, and Windows fanboys will probably have checked all four of them, then you give a humongous advantage to Windows.
But if you like Apple more, then you might disallow checking several options and then take the average of each system of a particular brand. Since Windows fanboys will be spread between more options, then the average of each option will be lower than it should, which will greatly disadvantage Windows over the others. You swine.
Moreover, when the time comes to analyze the data you gathered, you might tweak the results (to account for categories of people) in a way that makes one option win. Which is why you have to take measures to automatize the process, or get it done by a neutral third party (that doesn't know and cannot figure out from the data what the poll is about). It might be a good idea to throw random questions about other stuff in the poll, in order to hide which questions are relevant to the subject of the study.
Finally, the best way to increase truthfulness of your results is to subject them to peer reviews: a community of experts in the field impartially reviewing your study, its results, its analysis and its conclusions, in order to decide of its quality, rigorousness and objectivity, and whether it should be accepted, revised or rejected for publication.
The reviewers and their opinion can themselves be doubted, as impartiality is hard to reach. It helps, though.
In conclusion: whenever you find "groundbreaking new studies" about touchy subjects on the internet, there is a 99% chance that the results of the study are either inaccurate, wrong, falsified, greatly exaggerated, not significant, or taken out of context ("out of the 5 white male american vegetarian people who were asked, 3 said they were lactose intolerant, INCREDIBLE EVOLUTIONARY OUTBREAK! HUMANITY SOON WILL NOT BE ABLE TO DRINK MILK ANYMORE" published by WeLoveVeganism). The remaining 1% are results showing correlation between two events that is probably a total coincidence but YOU NEVER KNOW.
Science is a bitch. Still the best tool we have out there. Cherish it.
Last edited:
Sep 16, 2015 at 9:46 PM
Join Date: Jan 29, 2014
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Posts: 392
Age: 23
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At one point pretty much every Personal Computer was just called a PC. Later however, it came to mean any computer running on a x86 processor. Then, it came to mean only computers that ran MS/IBM-DOS or Windows. Now however, Mac DOES in fact use x86 processors after using PowerPC for years (i believe.) It still isn't a PC of course, as now it only means Windows. At one point, linux was considered a PC as well. In fact, many people still refer to a Linux machine as their PC.