1 / 5

Confirmatory Statistics: Identifying, Framing and Testing Hypotheses

Confirmatory Statistics: Identifying, Framing and Testing Hypotheses. Simon French simon.french@warwick.ac.uk. Cynefin and statistics. Unique events. exploratory analyses. Repeatable events. Events?. Estimation and confirmatory analysis. Value focused thinking.

missy
Télécharger la présentation

Confirmatory Statistics: Identifying, Framing and Testing Hypotheses

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Confirmatory Statistics:Identifying, Framing and Testing Hypotheses Simon French simon.french@warwick.ac.uk

  2. Cynefin and statistics Uniqueevents exploratoryanalyses Repeatable events Events? Estimation andconfirmatoryanalysis

  3. Value focused thinking • “Values are what we care about. As such, values should be the driving force for our decision making. They should be the basis for the time and effort we spend thinking about decisions. But this is not the way it is. It is not even close to the way it is.” Keeney (1992) • Define objectives, research questions, hypotheses at outset • (probably modify pragmatically as research progresses!) • More creative in research design • Focuses attention on what matters • Helps identify the ‘right’ research/problem solving methodology Note: whether we talk of objectives, research questions, hypotheses depends on type of research project

  4. Confirmatory Statistics • About checking whether data are compatible with some hypothesis or model • Note: • we can never show a model or hypothesis is ‘true’ • And seldom can we completely falsify it – randomness usually clouds the issue. • The processes are much more about weighing strength of evidence

  5. Hypothesis testing • Note that 5% significance level means that 1 in 20 tests will ‘reject the null hypothesis’ when it is true. • Thus if you perform lots of tests in your research you will necessarily make lots of mistakes!!!!! • There are theories of multiple testing to help avoid misinterpretation in such cases

More Related