BLA1447

1. Human sciences are "fuzzy" sciences, but they are still sciences.
Most scientists claim that Human Sciences because a lot of them can't be tested due to personal beliefs, religious ethics, etc., but they are still able to be observed- part of the scientific method, making it an actual science. Example: A doctor saying Psychology isn't a science because it doesn't meet all the requirements for a science- as if every science follows those rules. Psychology isn't a Science.

2. Science requires data.
Many human scientists can't provide true concrete data to support themselves on their claims so they just use their own personal beliefs and other biases try to prove themselves and fail as well.

3. Science runs on careful criticism.
When someone tries to argue with another scientists data just because they're opposed to the subject doesn't necessarily mean that you can prove whether it is right or wrong with other biased data. You can't really just say something will be discredited someday or not without plausible data. Example: Boys and girls having different brains. This particular scientists states that there is no difference, using sources that mean literally nothing. For example, "stereotypes was used... as if science can determine a stereotype or not. Boys' brains, girls' brains

4. Fight fire with fire, and data with data.
When one would like to argue with another scientists about something in detail, you can't simply just saying "there might be something wrong with that study". While there may be something wrong with that specific study, there's really no possibly way that one can go through it all and pick out the flaws and make it an actual true study. That's just really biased and that's where "Fight fire with fire, and data with data" comes in at. Example: **Monocausality ** is described in the second paragraph, only to be studying the differences between humans and rodents, while arguing that society can't effect someone's way of thinking. As if she can compare us to animals. Monocausality

5. There's power in precise terms.
Scientists like to complicate things as it has come to my attention. There are many different types of studies, ranging from sex differences to what's the difference between the words "hypothesis" and "theory". By definition, a theory is a summary of the hypothesis, as the hypothesis is an educated guess towards a specific experiment. However, there are many scientists that like to considerably argue that the two are the same. Below is an article saying the differences between the two with reasonable information to back it up. Theory vs. Hypothesis

6. Correlation is not causation.
The problem with a lot of different"data based answers" is that there may seem to have a lot of biased conclusions. For an example, just because a light causes nearsightedness doesn't mean every single night light causes nearsightedness in young children. The correlation between light and nearsightedness may be true, however, how can that be concluded as a cause to a young child's nearsightedness? There's really no way in concluding that without possible genetic testings. Below is an article where one scientist is blaming a night light for this child's eye problems, and not taking into consideration that he may have gotten it through genetics. Night Lights

7. More of something good isn't always better.
A lot of people nowadays assume more of a nutritional kind of food is necessarily "better" than less of a bad nurtional food. Absolutely not. If you get something that says "Tuna Salad on whole wheat bread", dietitians have crammed in our head that anything with the word "salad" and "whole wheat" is good for us. To be honest, anything that is per-packaged is not good for you. Just because you're eating something that has the last name salad doesn't mean its calorie count wouldn't be pushing that one of a big mac. Unhealthy foods