Question

Bring out the steps in Conducting Experiments and their concerned design

Answer 1

An experiment is a type of research method in which you manipulate one or more independent variables and measure their effect on one or more dependent variables. Experimental design means creating a set of procedures to test a hypothesis.

A good experimental design requires a strong understanding of the system you are studying. By first considering the variables and how they are related (Step 1), you can make predictions that are specific and testable (Step 2).

How widely and finely you vary your independent variable (Step 3) will determine the level of detail and the external validity of your results. Your decisions about randomization, experimental controls, and independent vs repeated-measures designs (Step 4) will determine the internal validity of your experiment.

Step 1: State your null and alternate hypothesis

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H_o) and alternate (H_a) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

Step 2: Collect data

For a statistical test to be valid, it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

Step 3: Perform a statistical test

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p-value. This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p-value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of data you collected.

Step 4: Decide whether the null hypothesis is supported or refuted

Based on the outcome of your statistical test, you will have to decide whether your null hypothesis is supported or refuted.

In most cases you will use the p-value generated by your statistical test to guide your decision. And in most cases, your cutoff for refuting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

Step 5: Present your findings

The results of hypothesis testing will be presented in the results and discussion sections of your research paper.

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p-value). In the discussion, you can discuss whether your initial hypothesis was supported or refuted.

In the formal language of hypothesis testing, we talk about refuting or accepting the null hypothesis. You will probably be asked to do this in your statistics assignments.

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