[MUSIC] The variable sound scareable, what a what's a variable. >> A variable is anything that can vary as in wow, that variable is rather variable. >> Okay, well in the interest of time can we stop this rhyme? Poetry is fun, and ryme is grand, but let's make sure that our learners understand. In this video we will learn about variables. What they are, how they are defined, and how they are used in the context of scientific research. [MUSIC] >> Okay then, let's start from the top. A variable is a noun is anything that varies, the verb to very tells us how different or spread out measurements of a set of variables maybe. The word variable can also be an adjective which describes how much a set of measurements varies. Variability is the extent to which measures are inconsistent or different. So variables with high variability have measures different from each other and those with low variability have similar measures. >> Variables measure change and we can talk about these differences in measurements using the word variable and related terms depending on the context. So maybe now that we know a bit more we can start trying to explore. So how do we know if a variable varies like the many shades of red and all of these cherries? >> First we must define our variables operationally and then based off are defined measures, we can keep a tally. >> Okay, wait, stop, stop the line again, I didn't think that everything had to be dissected by science. Well, of course, not everything goes under a microscope in science. The operational definition of a variable is based on your question of interest and defines operationally or functionally, what exactly you are measuring or manipulating to answer your question. So you want to measure the amount of variability in the shades of red of all these cherries. The variable is the color or shade of red, and for your variable of color to be measured, it must be operationally defined. So your variable could be operationally defined, for example by the measurement of the wavelength of light reflected off each cherry. Using operationally defined measurements, we can then answer your question of how variable the colors of Cherries are. If we have a widespread distribution where measurements are very different wavelengths, we could tentatively conclude that there is a high variability of colors of red for these cherries. If we have a tight distribution of measurements where most of the measures are similar to each other or clustered together, we could tentatively conclude that there is a low variability of colors of red for these cherries. >> Wait wait, wait, now let me get this straight. We define our variables and record our measurements and with that we can create some judgements. >> No, no, no, that's not it. Measurements of variables are good and fun. But in science we want meaningful measurements to answer a question for someone. >> To really say the question is the most important part of research or studying. Like almost research papers, have a question, begin with a question. And then develop a thesis around the question and then going fine facts. >> So here's an example, coming in hot, I've got a burning scientific question and answers I have not. But in science I can make predictions about the outcome of certain conditions. First I identify my independent variables, which defines my manipulations or conditions. Then I identify my dependent variables, which defines what I am measuring to test my predictions. >> Well, I see that does sound clever to define record and compare dependent measures across both independent conditions, will help me answer my scientific questions. Like if I hypothesize that I smile, when the sunshines, my dependent variable operationally defined is the number of my smiles between 3:00 and 4:00 in the afternoon, measured every day throughout the month of June. Then I'll define my independent variable condition as the times when the sun is out, versus when the sun is in. There is one last type of variable we have yet to encounter the bothersome, the infuriating. The annoying Confounder. >> Yes, confounding or extraneous variables create complications, influencing our dependent measures and or indypendent manipulations. Indeed, extraneous variables are hard to avoid, but this doesn't mean that all research measures and manipulations are devoid. >> Knowing that extraneous variables can be introduced anywhere from the temperature outside, to the style of my hair. Good scientists and critical thinkers often make note of the presence of possible extraneous variables interfering with the study scope. >> So confounding variables, although often not planned, provide researchers an thinkers extra variables to study and understand. >> I do have one last Q. How do I know if I've defined variables as well as others do? >> That's one thing that's left up to you. If you did a bad job, defining it will come up in peer review. Every scientist operationally defined variables differently. It depends on your question of interest in your predictions entirely. If you generate hypothesis falsifiably, it will clarify the variables that will prove your prediction wrong. You see, decide on the observations that would disprove your hypothesis. Define what you can measure, or measure what you can see and then your conclusions can be generated tentatively. >> But now I just want to make sure that you know all about variables before I go. So a variable is not that's scareable, but as you have come to see, variables can be variable in their utility. Having a strong understanding of variables, will support a strong understanding in research design. Now go forth, identify, define and try not to confound your independent and dependent variables. So do I smile more when the sun shines? Gotta go, I won't know until I finish this rhyme. [MUSIC]
