The scientific method is garbage. That’s right, I said it. Learning that things are done in this perfect, organized, step-by-step method, and everyone on the fucking planet does it the exact same way is utter bullshit. That’s not how people solve problems. Well, it is how we do stuff but its not. We don’t do things in these pretty little stages that you were probably taught when you were still learning how to multiply. Intuitively people solve problems. Some do it better than others but as a species, our brain is our greatest ally in surviving. If we’re presented with a puzzling situation, we try to fix it. If you connect your charger to your phone one night and go to sleep, you expect it to charge through the night and be at one-hundred percent in the morning. If you wake up (probably oversleeping) and the phone is dead, you don’t assume it was some witch’s curse that did that, you check that shit out. Was the charger plugged into the phone? Was the charger plugged into the wall? Was the charging cable intact? Did the power go out last night? That’s the scientific method.
So often, you see the scientific method broken into at least five different steps, sometimes many more. When you use phrases like “make careful observations,” “form a hypothesis,” “conduct an experiment,” “gather data,” and “draw conclusions,” it makes it seem like this drawn-out and formal process. In reality, “doing science” is much more straightforward than that. Using the cell phone analogy I described earlier, the hypothetical you conducted at least five different “rounds” of the scientific method. To really drive my point home (and increase the word count of this book,) I’ll prove it.
Round one:
Observation: My phone is about to die
Hypothesis: If I plug my phone in, then it's going to charge and be at one hundred percent by morning
Experiment: Plug the phone in
Gather Data: My phone is dead
Draw Conclusions: My phone did not charge
Round two:
Observation: My phone didn’t charge, even though I plugged it in
Hypothesis: Maybe my charger wasn’t fully plugged into my phone
Experiment: Check to make sure it was fully plugged in
Gather Data: Yup, was totally plugged in
Draw Conclusions: That's not the problem
Round three:
Observation: My phone didn’t charge, even though it was fully plugged into the phone
Hypothesis: Maybe my charger wasn’t fully plugged into the wall
(This is where I got bored of this cycle but you get the point)
As you can see, there are at least five rounds of the scientific method that you easily conducted within a thirty second period and never once did you think, “Oh shit, I have to revise my hypothesis now that the previous experiment I conducted yielded negative results.” So it doesn’t really matter that some people have described it has having five steps, or seven steps, or twenty five; all that matters is that you carefully do what you do and are methodical about collecting your data. The “bigger” or “more important” the experiment, the more detail oriented you need to be. The phone charging thing wasn’t a big deal so you didn’t have to record data and all that crap, you just did. But if you were doing legitimate lab work, or you’re a doctor trying to treat a patient, you want to make sure you keep detailed records so you don’t blow the place up or accidentally kill someone.
Here’s where I point out some stuff that to some, may seem like a contradiction to what I just finished describing. While for most people it won’t matter that you don’t know all the steps of the scientific method, there are some extremely important words and concepts that I need to cover. Most of them have to do with misconceptions people have or distinguishing between some VERY similar ideas that smart people like to (and sometimes need to) be very picky about how they’re used.
First up is the difference between an observation, an inference, and a hypothesis. Before you get “a hypothesis is an educated guess” on me, shut the fuck up, no it’s not. That’s why I’m explaining it and you’re reading this. Observations should be easy to understand: its just noticing and/or describing things. They can be super detailed or as simple as looking at something weird and going “huh?” These observations can lead to inferences, which are you educated guesses. Inferences take the information you have (usually gained through observations) and takes it to the next logical place. Or they fill in the blank that may be missing. If I tell you the sun is shining and its July 4th, you may infer that its hot outside and people are stuffing their faces and blowing shit up. That’s because you’re a selfish American and you assumed I was talking about people in the United States. If I were talking about the Southern Hemisphere, the fourth of July is winter and they dont give a shit about when the United States decided they didnt want to be part of Britain. So I like to think of inferences as logical assumptions that should be true but that don’t have to be true. This brings us now to everyone’s favorite, the hypothesis. Please, for the love of all things good and true, stop thinking of it as an educated guess. You’re not fucking guessing when you’re forming a hypothesis. Well, I guess you are but it’s not just a guess. With a hypothesis, you’re taking all your observations, all your inferences, all the information you have and you’re proposing a solution. If you’re oddly attached to thinking that a hypothesis is a form of guess, you need to think of it as the absolutely best fucking guess you have ever made in your life. The other big thing about hypotheses (not a typo, that’s the plural) is you have to be able to test them out. Thats the point of experimentation. You propose the hypothesis, and they you try to prove it. Before I go on, there will be some people out there right now thinking (or saying aloud if they’re weird) “Wait, wait, wait. You don't try to prove a hypothesis, you try to disprove it...blah blah blah null hypothesis blah blah blah.” Whatever. This is basic high school biology. They’re right but I need you to have a basic understanding of what the hell I’m talking about. If you want to get more technical, maybe pick up an actual science book and not one that literally calls you a bastard in the title.
Next distinction to be made is between control groups and experimental groups, which involves discussing variables. Hopefully you know what the word “varies” means because that’s what variables are; they are anything that can vary. Within experiments, we really have (or should have) three or four different types of variables: controlled, independent, dependent, and possibly confounding. With controls, you essentially try to standardize (or control…) these variables across all subjects so that they no effect on your experiment. All groups get the exact same amount of these variables so that everything is equal because eventually, the control group is going to be used to as a comparison to your experimental group. The control group and the experimental group are going to differ by a single variable, which is the one you’re testing and goes by the lovely name of the independent variable, although its street name is the manipulated variable. The point of your experiment is to determine the effects of the independent variable on another variable, which we call the dependent (or responding) variable. Lastly, confounding variables are bad; they’re variables that you should have controlled because they could have affected your experiment, but for whatever reason, you didn’t.
All this is best explained using an example and the easiest example is trying a new fertilizer on a plant’s growth. In order to see if this fancy new fertilizer works, you need to be able to compare the size of the plant that receives fertilizer with a plant that doesn’t get it. So you get two of the same plant (controlled variable one) and put them in the same sized pot (controlled variable two) with the same amount and type of soil (controlled variables three and four). You give the two potted plants the same amount of water (controlled variable five), light (controlled variable six), and expose them to the same temperature (controlled variable seven). At this point, we have to start calling the two plants different things, probably plant A and plant B, although it could be anything at all: control and experimental, 1 and 2, Jeff and Steve, Captain America and Ironman, the possibilities are endless. Whatever their names, we need to differentiate them because one is about to get the fertilizer (the independent variable) and the other won’t. So let's go with control and experimental since that’s what I’m trying to teach you. The control gets nothing more than we’ve already given it because we want it to be the baseline. The experimental plant gets the fertilizer and, very importantly, nothing else that the control plant doesn’t. By only changing this single variable, we can directly connect the fertilizer to any differences between the plants we observe. We hope to see a difference in growth when we compare the two plants so in this case, growth is the dependent variable because the amount of growth depends on the amount of fertilizer the plant receives. When it comes to tracking this data, we can categorize it as one of two types: quantitative or qualitative. Quantitative is commonly thought of as numbers and are things you can count, whether it's legitimately going one, two, three, etc or using a thermometer, speedometer, pedometer, cytometer, or any kind of -ometer. Qualitative on the other hand is more a descriptive form of data, things not able to be counted. Maybe the plant we’re testing has grown to be forty five centimeters (quantitative data) but the plants are spotted and droopy looking (qualitative data). As you can see, both forms are super important. At this point, I don’t feel like talking about confounding variables, so good luck with those. It’s time to move on to something else.
Round one:
Observation: My phone is about to die
Hypothesis: If I plug my phone in, then it's going to charge and be at one hundred percent by morning
Experiment: Plug the phone in
Gather Data: My phone is dead
Draw Conclusions: My phone did not charge
Round two:
Observation: My phone didn’t charge, even though I plugged it in
Hypothesis: Maybe my charger wasn’t fully plugged into my phone
Experiment: Check to make sure it was fully plugged in
Gather Data: Yup, was totally plugged in
Draw Conclusions: That's not the problem
Round three:
Observation: My phone didn’t charge, even though it was fully plugged into the phone
Hypothesis: Maybe my charger wasn’t fully plugged into the wall
(This is where I got bored of this cycle but you get the point)
As you can see, there are at least five rounds of the scientific method that you easily conducted within a thirty second period and never once did you think, “Oh shit, I have to revise my hypothesis now that the previous experiment I conducted yielded negative results.” So it doesn’t really matter that some people have described it has having five steps, or seven steps, or twenty five; all that matters is that you carefully do what you do and are methodical about collecting your data. The “bigger” or “more important” the experiment, the more detail oriented you need to be. The phone charging thing wasn’t a big deal so you didn’t have to record data and all that crap, you just did. But if you were doing legitimate lab work, or you’re a doctor trying to treat a patient, you want to make sure you keep detailed records so you don’t blow the place up or accidentally kill someone.
Here’s where I point out some stuff that to some, may seem like a contradiction to what I just finished describing. While for most people it won’t matter that you don’t know all the steps of the scientific method, there are some extremely important words and concepts that I need to cover. Most of them have to do with misconceptions people have or distinguishing between some VERY similar ideas that smart people like to (and sometimes need to) be very picky about how they’re used.
First up is the difference between an observation, an inference, and a hypothesis. Before you get “a hypothesis is an educated guess” on me, shut the fuck up, no it’s not. That’s why I’m explaining it and you’re reading this. Observations should be easy to understand: its just noticing and/or describing things. They can be super detailed or as simple as looking at something weird and going “huh?” These observations can lead to inferences, which are you educated guesses. Inferences take the information you have (usually gained through observations) and takes it to the next logical place. Or they fill in the blank that may be missing. If I tell you the sun is shining and its July 4th, you may infer that its hot outside and people are stuffing their faces and blowing shit up. That’s because you’re a selfish American and you assumed I was talking about people in the United States. If I were talking about the Southern Hemisphere, the fourth of July is winter and they dont give a shit about when the United States decided they didnt want to be part of Britain. So I like to think of inferences as logical assumptions that should be true but that don’t have to be true. This brings us now to everyone’s favorite, the hypothesis. Please, for the love of all things good and true, stop thinking of it as an educated guess. You’re not fucking guessing when you’re forming a hypothesis. Well, I guess you are but it’s not just a guess. With a hypothesis, you’re taking all your observations, all your inferences, all the information you have and you’re proposing a solution. If you’re oddly attached to thinking that a hypothesis is a form of guess, you need to think of it as the absolutely best fucking guess you have ever made in your life. The other big thing about hypotheses (not a typo, that’s the plural) is you have to be able to test them out. Thats the point of experimentation. You propose the hypothesis, and they you try to prove it. Before I go on, there will be some people out there right now thinking (or saying aloud if they’re weird) “Wait, wait, wait. You don't try to prove a hypothesis, you try to disprove it...blah blah blah null hypothesis blah blah blah.” Whatever. This is basic high school biology. They’re right but I need you to have a basic understanding of what the hell I’m talking about. If you want to get more technical, maybe pick up an actual science book and not one that literally calls you a bastard in the title.
Next distinction to be made is between control groups and experimental groups, which involves discussing variables. Hopefully you know what the word “varies” means because that’s what variables are; they are anything that can vary. Within experiments, we really have (or should have) three or four different types of variables: controlled, independent, dependent, and possibly confounding. With controls, you essentially try to standardize (or control…) these variables across all subjects so that they no effect on your experiment. All groups get the exact same amount of these variables so that everything is equal because eventually, the control group is going to be used to as a comparison to your experimental group. The control group and the experimental group are going to differ by a single variable, which is the one you’re testing and goes by the lovely name of the independent variable, although its street name is the manipulated variable. The point of your experiment is to determine the effects of the independent variable on another variable, which we call the dependent (or responding) variable. Lastly, confounding variables are bad; they’re variables that you should have controlled because they could have affected your experiment, but for whatever reason, you didn’t.
All this is best explained using an example and the easiest example is trying a new fertilizer on a plant’s growth. In order to see if this fancy new fertilizer works, you need to be able to compare the size of the plant that receives fertilizer with a plant that doesn’t get it. So you get two of the same plant (controlled variable one) and put them in the same sized pot (controlled variable two) with the same amount and type of soil (controlled variables three and four). You give the two potted plants the same amount of water (controlled variable five), light (controlled variable six), and expose them to the same temperature (controlled variable seven). At this point, we have to start calling the two plants different things, probably plant A and plant B, although it could be anything at all: control and experimental, 1 and 2, Jeff and Steve, Captain America and Ironman, the possibilities are endless. Whatever their names, we need to differentiate them because one is about to get the fertilizer (the independent variable) and the other won’t. So let's go with control and experimental since that’s what I’m trying to teach you. The control gets nothing more than we’ve already given it because we want it to be the baseline. The experimental plant gets the fertilizer and, very importantly, nothing else that the control plant doesn’t. By only changing this single variable, we can directly connect the fertilizer to any differences between the plants we observe. We hope to see a difference in growth when we compare the two plants so in this case, growth is the dependent variable because the amount of growth depends on the amount of fertilizer the plant receives. When it comes to tracking this data, we can categorize it as one of two types: quantitative or qualitative. Quantitative is commonly thought of as numbers and are things you can count, whether it's legitimately going one, two, three, etc or using a thermometer, speedometer, pedometer, cytometer, or any kind of -ometer. Qualitative on the other hand is more a descriptive form of data, things not able to be counted. Maybe the plant we’re testing has grown to be forty five centimeters (quantitative data) but the plants are spotted and droopy looking (qualitative data). As you can see, both forms are super important. At this point, I don’t feel like talking about confounding variables, so good luck with those. It’s time to move on to something else.
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