With the results of our experiments (whether natural or laboratory-based) in hand, the scientists can explicitly test their hypothesis, and either reject and revise it, or tentatively accept it. A common way to conduct this test is by examining graphs relating independent and dependent variables on a graph. Recall that by convention, scientists generally put the independent variable (if there is one) on the X-axis of a graph, and the dependent variable on the Y-axis. In this experiment, is the independent variable and is the dependent variable.
In this experiment, the independent variable is the factor or condition that is intentionally manipulated by the scientists. It is the variable that is believed to have an effect on the dependent variable.
The dependent variable, on the other hand, is the variable that is measured or observed in response to changes in the independent variable. It is the variable that is expected to change as a result of manipulating the independent variable.
To further clarify the statement, the independent variable can be labeled as "X" and the dependent variable can be labeled as "Y" on a graph. This convention helps to visually represent the relationship between the variables and allows scientists to analyze the data and draw conclusions about their hypothesis.
If the data are consistent with their prediction, the scientist accepts the hypothesis, but only provisionally, as they know future experiments may require the hypothesis to be revised or even abandoned. It is also possible the data are inconsistent with the prediction, in which case the scientist revises the hypothesis and goes through the process again. This “self-correcting” methodology is at the heart of science’s ability to explain how the natural world functions.
Based on this hypothetical data you’ve seen, is the hypothesis correct?
Yes, the data is consistent with the prediction, which means that you accept the hypothesis as being supported by the data.
No, the data contradicts the prediction, which means that this hypothesis is incorrect and needs revision.
Based on the information provided, it is not possible to determine whether the hypothesis is correct or incorrect without examining the hypothetical data. The determination of whether the hypothesis is correct or not would depend on the specific results obtained from the experiments and their analysis.
If the data are consistent with their prediction, the scientist accepts the hypothesis, but only provisionally, as they know future experiments may require the hypothesis to be revised or even abandoned. It is also possible the data are inconsistent with the prediction, in which case the scientist revises the hypothesis and goes through the process again. This “self-correcting” methodology is at the heart of science’s ability to explain how the natural world functions.
Based on this hypothetical data you’ve seen, is the hypothesis correct?
A. Yes, the data is consistent with the prediction, which means that you accept the hypothesis as being supported by the data.
B. No, the data contradicts the prediction, which means that this hypothesis is incorrect and needs revision.
Based on the provided hypothetical information, if the data are consistent with the prediction, the condition would be:
A. Yes, the data is consistent with the prediction, which means that you accept the hypothesis as being supported by the data.
In this experiment, In this experiment, presence or absence of wasps in the greenhouse
the amount of plants infected is the independent variable and is the dependent variable.
presence or absence of wasps in the greenhouse
the amount of plants infected
In this experiment, the independent variable is the presence or absence of wasps in the greenhouse. It is the factor that is intentionally manipulated by the scientists.
The dependent variable is the amount of plants infected. It is the variable that is measured or observed in response to changes in the independent variable.
Please note that the variable names were not fully provided, so the blank spaces are left as is.
Which of the following hypothetical situations correctly tests the prediction listed above and includes both an experimental group and a control group?
In 2 different greenhouses, plant 40 cassava plants, and infect each plant with mealybugs. Introduce wasps into both greenhouses. Some time later, observe how many plants in each greenhouse have mealybug infections.
Distribute a survey to farmers, asking each to estimate how many of their plants have mealybug infections, and if they have noticed wasps in their fields.
In 6 different greenhouses, plant 40 cassava plants, and infect each plant with mealybugs. Introduce wasps into three of the (randomly selected) greenhouses, but not the others. Some time later, observe how many plants in each greenhouse have mealybug infections.
The hypothetical situation that correctly tests the prediction and includes both an experimental group and a control group is:
In 6 different greenhouses, plant 40 cassava plants, and infect each plant with mealybugs. Introduce wasps into three of the (randomly selected) greenhouses, but not the others. Some time later, observe how many plants in each greenhouse have mealybug infections.
This situation establishes an experimental group (the greenhouses with introduced wasps) and a control group (the greenhouses without introduced wasps), allowing for a comparison between the two groups to determine the impact of the independent variable (presence or absence of wasps) on the dependent variable (number of plants with mealybug infections).
To determine the independent variable (X-axis) and dependent variable (Y-axis) in an experiment, you need to understand the purpose of the experiment and the relationship between the variables being studied.
The independent variable is the variable that the researcher intentionally manipulates or controls in order to observe its effect on the dependent variable. It is often denoted as "X" and is typically plotted on the X-axis of a graph.
The dependent variable, on the other hand, is the variable that is being measured or observed to determine the impact of the independent variable. It usually represents the outcome or response that is expected to change based on the manipulation of the independent variable. The dependent variable is represented as "Y" and is typically plotted on the Y-axis of a graph.
In the provided statement, the independent variable is missing ("_") and the dependent variable is also missing ("_"). To identify the specific independent and dependent variables in this experiment, you would need to provide the missing information. Once you have determined the variables, you can plot them on a graph accordingly to analyze the relationship between them and draw conclusions from the experimental results.