Grant Writing: Developing a Testable Hypothesis

Detailed Evaluation Results for Grant Writing: Developing a Testable Hypothesis

38 of the 47 (20 women, 27 men) faculty that attended completed evaluations.

Central to most research endeavors is the underlying hypothesis being addressed. It is not uncommon for an investigator to propose a hypothesis that is fundamentally not testable. This presentation explored the issues underlying this situation and examined different and actual scenarios volunteered by faculty, to help determine what makes the resulting hypotheses testable.

Faculty felt that the workshop aided them in improving their ability to develop a testable hypothesis. Survey results showed that participants learned the importance of clear measurable data and careful working of a hypothesis. "If there is no data, there is no test." They mentioned that they will "think carefully about making the hypothesis specific enough to align with the outcomes" and that "how you use language is important in making your ideas clear."

Overall, faculty thought it was a "great topic" and appreciated the examples and analysis of the hypotheses. However, they also suggested that in the future, they would like to have "more discussion" and actual "examples of good testable hypotheses from successful grants."

Questions & Responses

The workshop lived up to my expectations.
N=38 Mean=3.97 SD=1.05 Range= 1 (strongly disagree)- 5 (strongly agree)

The information I learned at this workshop will improve my ability to develop a testable hypothesis.
N=38 Mean=4.24 SD=0.85 Range= 1 (strongly disagree)- 5 (strongly agree)

This session prepared me to anticipate difficulties in formulating hypotheses and provided useful tools for overcoming obstacles.
N=38 Mean=4.13 SD=0.28 Range= 1 (strongly disagree)- 5 (strongly agree)

I am glad I took the time to participate in this presentation.
N=37 Mean=4.38 SD=0.74 Range= 1 (strongly disagree)- 5 (strongly agree)

Questions & Answers:

Please give a specific example of at least one thing you learned and will definitely take from the Grantwriting: Developing a Testable Hypothesis workshop to use in your work.

  • There needs to be things that can be measured without ambiguity.
  • How language is important to making your ideas clear.
  • I will think carefully about whether the hypothesis is specific enough to sensibly align with the experimental outcomes.
  • If there is no data, there is no test.
  • Writing a hypothesis which can’t be refuted based on lack of data! No data, no test!
  • Be careful about data to be collected.
  • Clear statement of measurable factor.
  • Great topic!
  • I learned that I have been writing sequential hypotheses that may be difficult to interpret.
  • Design is more important than hypothesis. Any little mistake in a grant application may cause your research to NOT get funded.
  • The hypothesis should be measurable.
  • Clarification and careful wording of the hypothesis, in particular the use of sequentially stated hypotheses.
  • Developing a testable hypothesis is not JUST a statistical question.
  • Be careful not to lump too many hypotheses into one.
  • Be specific and measurable.
  • Including specific measurable language.
  • I deal with development projects and a large part is trying to gauge the effect of the project - there are plenty of situations where the specifics can be explored.
  • The importance of data availability and quality. The specificity and testability of the hypothesis.

Is there anything that could have been improved about this workshop?

  • A little longer and more examples about what are good examples.
  • Is there more I need to know about this? I'm assuming we finished early because the speaker is very concise.
  • A little bit longer discussion.
  • Examples of rewording a hypothesis to make it stronger.
  • More discussion.
  • Examples of good testable hypotheses from successful grants.
  • More relevant content.
  • What makes a strong hypothesis?
  • A few more solutions, or best practices.
  • More interaction. Q&A may have generated cross-discipline discussion.
  • I wanted a little more "meat"/substance. Good talk but a little too basic.
  • Some examples worked out - tables, graphs and conclusion.
  • Some references or readings.

General Comments about the overall workshop?

  • I enjoyed it.
  • Thanks.
  • Good examples and analysis of examples.
  • Interesting.
  • Short + Sweet.
  • Was excellent!
  • Great!
  • Great for people in (almost) all the disciplines.