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A farmer decides to test whether or not a fertilizer will make her crops grow bigger. In year 1, she plants corn in two different fields on her property and fertilizes one and not the other. She measures the height of 10 corn plants in meters from each field. She takes her measurements every week for three months. In year 2, she puts irrigation in one of the fields and conducts the same experiment.

Required:
a. What is the independent variable and the treatments (increments) used in the experiment?
b. What is the dependent variable and the units in which it was measured?
c. Name at least two controlled variables:
d. What is the hypothesis?
e. Identify two sources of error in the first year in the experiment:
f. Identify two sources of error in the second year in the experiment:

Sagot :

marianaegarciaperedo

Answer:

a. independent variable: the fertilizer.

   treatments: the use of fertilizer in one of the fields.

b. dependent variable: the crop growth in height.

   units: meters

c. two controlled variables: soil pH / solar-radiation exposure, and water supply  

d. hypothesis: the fertilizer makes the crops grow bigger.

e. two sources of error in the first year:

  • the researcher might plant the two crops under different conditions
  • take wrong measures of the height of the corns

f. two sources of error in the second year:

  • she can fail in irrigating the crops
  • climatic and environmental conditions might change and have different consequences on each of the fields (this source affects the results influencing the growth of the plants).  

Explanation:

  • Independent (manipulated) variable: Refers to all the variables in an experiment that provoke a response in another variable. An independent variable is the one that changes or is controlled and modified in the experiment to analyze how another variable responds to it. These changes allow analyzing its effects on the dependent variable. Usually, the independent variable is represented by the X letter. In the exposed example, the fertilizer is the independent variable.    
  • Treatments: Refers to the experimental procedure applied in the experimental group. In this example, the use of fertilizer is the treatment. Here the experimental group (the one that receives the experimental procedure, with changes in the independent variable) is the fertilized field. Data from the experimental group is compared with the data from the control group, to analyze the effects of the fertilizer.
  • Dependent variable: The values of these variables respond to any change in the independent variable. It represents the quantity of something. The change in the dependent variable might be proportional or inversely proportional to the change in the manipulated variable. It is usually identified by the letter Y. In the exposed example, the crop growth rate in height is the dependent variable, that depends on the fertilizer used in that field. The units in which the crop is measured are meters in height.        
  • Controlled variable: Refers to those variables equally applied to every group or subject in an experiment and have no influence on the results. These variables do not affect the change in the dependent variable values. In the exposed example, soil pH and solar-radiation exposure can be two controlled variables, as they must be equal for both fields. Water supply by irrigation ducts during the second year in both fields is also a controlled variable.    
  • Hypothesis: A hypothesis is a possible answer for a question, a speculation that is not verified yet and requires corroboration. A hypothesis must express what is expected to occur in a perfectly comprehensive manner. It must be objective and directly related to variables. In this example, the hypothesis might be that the fertilizer makes the crops grow bigger.          
  • Errors: These are the differences between the observed data or taken values and what is really happening in nature, which can lead to a misinterpretation of what is actually going on. These errors might be systematic mistakes performed by the researcher when measuring, taking data, applying the treatment, etc. Or they might be due to random errors, which are due to failures in the instrumentals, changes in the environment,  a single mistake of the researcher while taking measures, among others. During the first year, the researcher might plant the two crops under different conditions (parcels with different slopes which affect solar-radiation) or might commit a mistake while applying the fertilizer (different concentrations for example), or might take wrong measures of the height of the corns. During the second year, she can fail in irrigating the crops correctly, providing more water to one of the fields.  She can commit the same measuring mistakes. Or even climatic and environmental conditions might change and have different consequences on each of the fields.        
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