It sounds like you are starting to learn the scientific method!

You can think of an independent variable is the variable that is being varied by you the experimenter or scientist; it is the input/"cause"/the tunable variable. A dependent variable is the response/output/"effect".

There is also the control variable, which are variables that stay constant throughout. You generally have only one independent variable and many control variables so that you can properly disentangle cause and effect.

A simple example might be say you want to know if a certain brand of fertilizer helps your crops grow. You want to see how much fertilizer you need for optimal growth without using too much. What is the independent and dependent variable? (Highlight below for the answer!)

The independent variable is the amount of fertilizer you put on the crop. It is the variable that you, as the tester, vary yourself. The dependent variable is how much your crop grows. What about the control? It is the crop you choose to use the fertilizer on (e.g., corn, wheat, peas). Certainly, comparing the influence of fertilizer between corn and peas would not definitely tell you whether the fertilizer is effective; if you compared two different crops, you would have other confounding variables out of your control, so you want to test it only on one crop at a time.

I put cause and effect in quotes because you may run into one of many cause/effect fallacies (or mistaken beliefs). It is possible (and often happens in research) where you plot what you think is an independent and its dependent variable, but the reality is quite different! There are two common types- spurious relationships and spurious correlations. A spurious relationship is one in which a direct cause/effect relationship is concluded incorrectly when in fact there is a lurking or hidden variable unaccounted for. One commonly cited example is say you find an increase of death by drownings and ice cream sales. It would be incorrect to infer that eating more ice cream causes more drownings, or vice versa. The lurking variable that can explain both is hot weather (i.e., people eat more ice cream and go swimming more to cool down). A spurious correlation is incorrectly inferring the extent of how related two variables are (not necessarily a cause/effect relation, but something used often in statistics). Here is a website that illustrates quite ridiculous correlations (a pet project of a graduate student), emphasizing that correlation is not causation.

You can find more examples of differentiating between dependent and independent variables here at the Kahn Academy. It's a great resource with short video clips and practice exercises on a variety of topics. I'll also include these Java applets in physics, chemistry, math, and more from the University of Colorado Boulder where the independent variables are actually nobs you can tune yourself to see the response!

Hope this helps!
Best,

A dependent variable relies (depends) on other variables to get its value. An independent variable doesn't rely on any others to get its value. Think of if you were looking at how temperature changed with the time of day. It's hotter in the middle of the day than at either morning or night. So, the temperature depends on the time. So, the temperature is dependent, and the time is independent. If you're looking at a graph, the independent variable is generally on the horizontal axis. Also, when you're looking at an equation, the independent variable is usually on the side that has other numbers and arithmetic.

So if you have y = 3x + 2, x is independent since y depends on it and is isolated.

Sometimes, to tell which is independent or dependent the equation will be written like y(x) = 3x + 2. This y(x) means y(depends on x) and x is independent.

I hope this helps! It took me a long time to get this figured out myself, at least until high school.

The short answer is that you can control an independent variable directly, but you can’t for a dependent variable.

Imagine you are heating up a pot of water on the stove. You can move the dial to different heat levels; this is an independent variable. However, the temperature in the pot of water is a result of you turning a dial so you can’t control it directly. This makes the temperature of the water a dependent variable.

As an experiment you might start with a pot of water at room temperature and heat it up on the stove. You could set the dial to low, medium or high and measure the temperature. So your independent variable could take on the values low, medium or high. But the temperature could take on many values and if you repeated the experiment multiple times, you would find that the temperature would be slightly different every time. What makes the dial position an independent variable is that you know completely which values it can take on since you set them.

Usually we talk about independent and dependent variables with experiments or equations.

An independent variable is the variable that we change in experiments or equations. Usually this is the "X" axis of a graph. The dependent variable is the result of the experiment or equation.

Imagine a simple experiment:
You want to see how far you can throw rocks. So you find rocks of different sizes and throw them. You notice that you can throw lighter rocks farther than heavier rocks.

In this case, the independent variable is how heavy the rock is. This is what you are changing in the experiment.

The result of the experiment (how far you throw the rocks) depends on how heavy the rocks are. So we say that how far you throw it is the "dependent variable"

When we make graphs, we normally put the independent variable on the "x-axis" and the dependent variable on the "y-axis."

The independent variable is a variable that does not depend on other variables. It has no other variables that are causing it (although it may be causing other, dependent, variables, itself).

It's also a convention of how you draw graphs - the independent variable is usually on the horizontal (x) axis, while the dependent variable is usually on the vertical (y) axis.

Independent variables are variables that represent "inputs" that you can change to see if there is an effect. It may be helpful at least very superficially to think about cause and effect statements when you are first trying to distinguish between independent variables and dependent variables. For example: "If I give a plant water, then will I see growth in the plant?" Here, the independent variable is giving plant water (the "cause" part of the sentence). You can choose to give the plant water or not.

The dependent variable is the growth in the plant. Your choice of giving the plant water or not results in some effect in the growth of the plant. I hope that helps!

And independent variable is something that you control, on purpose. You choose to change the independent variable, to see what the effect is on something else, called the dependent variable.

For example, if you wanted to see how the temperature changes as a function of season, the month could be the independent variable, and the temperature would be the dependent variable, or the parameter that _depends_ on the month (the independent variable). There could be many dependent parameters for any independent parameter you choose.

For example, if you chose someone's age as an independent variable (you're going to look at a population and see how things change with the age group you look at), you could pick just about any dependent variable you want: intelligence (do people get smarter as they get older?), height (do people grow as they get older?), weight (do people get heavier as they get older?), and so on.