True
A treatment
Positive controls : an experimental treatment that will give the desired result Negative controls: An experimental treatment that will NOT give the dersired result.
A repetition of an experiment is used to provide reliability. Just in case your result was a coincidence or caused by another factor not your treatment. Examples of repetition can be having more than one thing in each treatment. E.g. Having four plants with high clay soils in case something unexpected happens. Like one doesn't germinate. Another example could be that you do the experiment a couple of times so that you are sure that your results is because of the variable/treatment you are testing. Hope this helps.
yes It sounds like you have a good handle on this based on the question..
True
Response bias cannot be eliminated, but it should cancel out between the treatment and control groups.
The common types of randomization include simple randomization, block randomization, and stratified randomization. Simple randomization involves assigning participants randomly to treatment groups with each having an equal chance of being selected. Block randomization involves grouping participants into blocks and then randomly assigning them to treatment groups within each block. Stratified randomization involves dividing participants into distinct subgroups based on specific criteria and then randomizing within each subgroup.
treatment is a factor in which a researcher will apply to an experimental unit and collect the data from the same. factor is a material used by researcher in an experiment in the field .
A control variable is a factor that is held constant in an experiment to prevent it from influencing the outcome. A control treatment, on the other hand, is a specific group or condition in an experiment that receives no experimental manipulation and is used as a baseline for comparison with the treatment groups.
The placebo effect can lead to incorrect results in an experiment by causing participants to report improvements in their condition, even if they are receiving a treatment that is ineffective. This can mask the true effects of the treatment being tested. Additionally, participants' expectations and beliefs can influence their responses, leading to biased outcomes.
Placebos are used in experiments to distinguish the effects of a treatment from those that might occur simply due to expectation or the act of receiving medical attention. By comparing the responses of participants who receive a placebo with those who receive the actual treatment, researchers can determine the true effectiveness of the treatment.
In a controlled experiment, there are two groups. The control group is a group that nothing happens to. The experimental group is the group that you subject to the variable with which you are experimenting. At the end of the experiment, you test the differences between the control group, for whom nothing happened, and the experimental group, which received the variable. The difference (or similarities) between the two groups is how your results are measured.A control group is the group used for comparison in an experiment. One group receives the treatment that is being tested by the experiment; another group (the control group) has the exact same controlled environment, but does not receive this treatment. The effectiveness of the treatment can then be established by comparison with the control group.
The group in an experiment that receives the treatment is called the treatment group. This group is exposed to the intervention or variable being tested to determine its effect. It is compared against a control group that does not receive the treatment to evaluate the efficacy of the treatment.
the control.
they are the things that don't change in an experiment/ that don't receive any special treatment.
When setting up an experimental procedure one prepares a control treatment as well as one or more experimental treatments. At the end of the experiment, if there is no difference between the experimental and control groups the experiment is typically said to be not conclusive. With a typical set-up, this result generally fails to lead to a rejection of the null hypothesis.