To ensure an experiment is reliable, it's important that sampling is random. This implies that the participants of the experiment are randomly assigned to the treatment and the control group, which reduces the risk of bias and produces groups with similar characteristics.
For example, a control group with ten men and a treatment group with ten women might not be reliable. This is because, any results could be contributed to the difference in gender rather than the treatment itself. Instead, the test subjects should be randomly assigned to each group so there are the same number of men and women.
The development team at the company WoofWoof has created a new dog food. The developers claim it makes the hair of dogs grow faster. To see if it works, they've designed an experiment. They let 10 dogs from a dog breed with short hair eat only the new food. At the same time, 10 dogs from a breed with long hair only eat the normal food. State the experiment's:
Finally, determine if the test subjects are randomly assigned to the treatment and the control group.
After an experiment has been conducted, it is necessary to analyze the data. The results obtained from the treatment group are compared with the data from the control group. For example, see the results from an experiment that studies the growth of seedlings from irradiated apple seeds. Five seeds were irradiated — exposed to radiation — and then planted. The data is compared with a control group of five non-irradiated seeds.
|Seedling length (cm)|
Making inferences involves using data from a sample to draw conclusions or make predictions about a population. There is always some degree of uncertainty when the results from a study are used to make inferences. However, increasing the size of the sample reduces uncertainty.
Below, the recorded lengths of 100 irradiated and 100 non-irradiated apple seeds are shown.
|Length (cm)||Treatment group||Control group|