Variables

The manipulated (independent) variable is the single factor that the researcher systematically varies across different experimental conditions to determine its effect on the biological system.

This variable must have at least two different groups or levels to allow for meaningful comparison; these are the different values, conditions, or treatments of the independent variable that are tested in the experiment.

For example, if investigating the effect of temperature on enzyme activity, temperature is the manipulated variable, and the different groups might be 20°C, 30°C, 40°C, and 50°C. Each temperature represents a different level of the independent variable, and the enzyme activity at each temperature level will be measured and compared. Everything else must remain identical between these groups so that only the manipulated variable changes from one group to the next.

Information about controlled variables and validity measures is available here.

The responding (dependent) variable is the measurable outcome or response that changes as a result of the  manipulated variable. This is what the researcher observes, measures, or records to determine the effect of the experimental treatment. The responding variable should be quantitative whenever possible (measured with numbers and units) rather than qualitative (descriptive observations) to allow for statistical analysis and more precise conclusions.

For example, when studying the effect of light intensity on photosynthesis rate, the responding variable might be the volume of oxygen gas produced per minute, the rate of carbon dioxide consumption, or the change in pH of the solution.

Repeated trials are essential when measuring the responding variable. Each group or level of the manipulated variable must be tested multiple times (typically at least five trials) to account for natural biological variation and measurement uncertainty. These repeated measurements allow researchers to calculate mean values, identify outliers, assess the reliability of results, and perform statistical analyses to determine if observed differences are significant rather than due to random variation. 

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Categorical Variables
Variables that are qualitative groups. 
Categorical variables can be either nominal or ordinal.  

Nominal variables can not be ranked or ordered into a logical hierarchy.  For example: 

• Color (red, orange, yellow)
• Chemical property (polar, non-polar, charged)
• Type (oak, maple, cedar, fir)

Ordinal variables can be ranked or ordered into a logical hierarchy. For example:

• pH (acidic, neutral, basic)
• Life stage (egg, embryo, baby, child, teenager, adult)​​

Numerical Variables
Variables that are quantitative (numbers). 
Numerical variables can be discrete or continuous. 

Discrete variables can not be divided into smaller increments.  For example: 

• count of the number of individuals in a population
• counting the leaves on a plant
• count of bacteria colonies

Continuous variables can be divided into smaller increments if a more precise tool was used.  For example: 

• temperature 37° C, but with a better thermometer the temperature could be measured as 37.4° C ​