Identify independent, dependent, and controlled variables in an experiment, and read a graph of the resulting data.
A controlled experiment isolates one cause at a time. The independent variable is the one thing the experimenter deliberately changes. The dependent variable is what is measured, which may or may not respond to that change. Everything else that could affect the outcome is held constant -- these are the controlled variables -- and a control group receives no treatment at all, giving a baseline to compare against. Running multiple replicates (repeated trials) matters because any single trial can be thrown off by chance; averaging several trials makes a real pattern easier to trust.
Consider a real design: does the amount of light affect how tall bean seedlings grow? Several identical cups get the same soil, the same amount of water, and the same starting seed type and count -- those are the constants. One group sits in full sunlight, one in dim light, and one in darkness -- light level is the independent variable. Stem height, measured every two days for two weeks, is the dependent variable. A full-light group serves as the control, representing normal growing conditions. The resulting data is plotted as a line graph, one line per light condition, with day number on the x-axis and average height on the y-axis, so the three conditions can be compared directly at a glance.
Mastery looks like: They can identify the independent variable, dependent variable, constants, and control group in a described experiment, and correctly read a labeled line graph.
Common stumbles: Confusing which variable is independent vs. dependent; forgetting that a control group needs everything else the same except the one variable being tested.