Interpreting Results

Guidance for interpreting and reporting the results of your project are provided below.

In the results section you report on just the objective “facts and figures” of what you found. You then interpret these results in the discussion section.

You have to report the results of your project or study in relation to your research questions/hypothesis. Present the results of the outcome variable(s) for each hypothesis.

Some guiding questions to consider when explaining your results:

  • Do the results agree with the ideas that you introduced in your proposal?
  • How do the results relate to previous literature or current theory?
  • Discuss any of the limitations in the study design that may reduce the strength of your results.

Use descriptive language to indicate the strength of the evidence.

p-value

Description

Strength of Evidence

 < 0.001

Extremely significant    

Very strong evidence against the null hypothesis in favor of the alternative.

0.001 – 0.010      

Highly significant

Strong evidence against the null hypothesis in favor of the alternative.

0.011 – 0.050

Significant

Moderate evidence against the null hypothesis in favor of the alternative.

0.051 – 0.100

Not significant

Weak evidence against the null hypothesis in favor of the alternative.

> 0.100

No evidence

No evidence against the null hypothesis.

This is where you explain the extent to which your study is externally valid. Discuss strengths and weaknesses of applying your results to, for example, another population, species, age, or sex.

Based on your results, and considering the study's limitations, introduce new ideas or ways to improve the current area of research.

Try to identify and discuss factors or conditions that may have contributed to unexpected results. For example, site conditions (e.g. room temperature) could have been different between two focus group sessions. 

Be careful about drawing erroneous conclusions. Report only actual findings and the relationships and associations between outcomes and predictor variables that have been confirmed with statistical evidence. For example, just because you find that two variables are related, you cannot automatically leap to the conclusion that those two variables have a cause-and-effect relationship.

Refrain from generalizing your results to a larger group than was actually represented by your study. For example, results from a study involving nursing students may not be applicable to registered nurses.

 

Resist the temptation to deviate or to make sweeping generalities based on your findings.

Summarize the study’s strengths, conclusions, implications, and your suggestions for future research.