Q1: Null - Tomato plant height is not positively correlated to sunlight exposure. Alt - Tomato plant height is positively correlated by the amount of sunlight they are exposed to. Q2: A shade structure is created to test the predicition that the shade contraption tomatoes will be shorter than the full sun tomatoes. I predict the shady contraption tomatoes will be shorter than the full sun tomatoes. Q3: The mirror contraption is to test the opposite of the shade contraption in order to ensure that sunlight is variable affecting height. Q4: This experiment shows that there might be a confounding variable that is affecting the tomato plant height, since the shade and mirror contraptions did not make the tomato plants shorter and taller (respectively) when compared to their full shade/sun counterparts as the alt hypothesis assumed. Q5: This scenario also does not support the alternative hypothesis because the plants under the shade structure were the same height as those in the sun. This suggests that there needs to be further trials conducted and more control variables added to reduce the amount of confounding variables that might be affecting the results. Q6: I am assuming that the plant height measurements are taken from overall plant heigh rather than stem length, as well as the shade and mirror contraptions giving the same amount of sunlight as the shade and sun areas. I am also assuming that the soil is not even being considered as a variable, hence why the results seem to not be lining up as I would expect. My prior knowledge of photosynthesis is also another assumption that is affecting my judgement, since it influences how I would assume the plants to react to sunlight.

It means that the experiment has leaned towards supporting the alt hypothesis rather than the null (I believe alt is like the 1 supports the stated hypo and null the opposite/nothing happened?), but hasn't proven it to be true or false. Falsifying hypotheses is an acknowledgment that even if the outcome seems to support one claim more than the other, it does not necessarily mean it is true because we don't know if it actually is. It also leaves space for people to question the seemingly supported conclusion and maybe even prove it wrong. A good example is people believing leeches were a good way to keep healthing. Now we have contradicting evidence to that claim thanks to our advancements.

Yes, it is possible for multiple people to see the same thing and interpret it differently. Example is I see water recurring on the bathroom floor and think "huh, water", while my mother comes and thinks "leak". It was in fact a leak. In this case, my mother had the background of many years of experience dealing with similar matters and knew what to look for. I percieved the fact that water was on the floor, but lacked the background and brainpower to connect it to a pipe. A random thought here, without well-structured problems, we cannot have well-structured solutions. And without well-structured solutions, we cannot have a universal application of this knowledge and therefore becomes a standalone concept, unable to be associated with other concepts or situations, even if the problems are virtually identical. The real world is full of ill-structured problems and I wonder what makes them ill-structured to begin with? Is it the sheer amount of variables involved? Probabilities? Or something else? If our models are advanced and comprehensive enough, can we actually make all ill-structured problems into structured? I am thinking specifically of the mathematical model created to simulate how people on the street walk and how they change their paths based on where other people are. Ok, thats enough.

In my life, it happens quite often. Just the other day I was chatting with someone and told them I had to leave and do some work. Later I realized that they thought I a job instead of me having to finish cleaning. This had an unintented consequence of misleading this person into thinking I was a diligent, hardworking individual, and that I had my own income. The use of vague language led to a misunderstanding as we both have different definitions of what work is.

I think I remember my Chemistry class often saying that a "molecule likes to be with another molecule", or that water doesn't like oil, for example. For everyday surface level discussions, this type of language is alright because it gets the point across in a way that everyone can understand. People are humans after all, so the easiest way to describe something is to use human interactions and terms. However when one wishes to dive a little deeper, these descriptions are very vague and do not actually say much about why something is happening. Taking the water and oil example, there are a lot of details being skipped, such as the atomic structures, the polarizations, the electron configurations etc etc that are not even being mentioned in the anthropomorphic terminology. Basically, when explaining something in terms of human behavior, one must sacrifice details and levels of deeper understanding.

I interpret "mental models" as a unique way for each individual to perceive a complex system in a more easily digestible way. Basically, a condensation of information to better break down a big idea into smaller chunks. I think it is quite important because the real world is infinitely complicated, so by visualizing little pieces of the bigger picture, can one eventually see the whole system.