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Zoekresultaten (13)

HBO/WO

MBO

20 oktober 2021

4_Information error: surveys - continuous variable

Information error - Implications of information error for surveys

Video

edumedia@wur.nl

20 oktober 2021

7_Information error: Analytical studies - continuous variable

Information error - Implications of information error for analytical studies - continuous variable

Video

edumedia@wur.nl

20 oktober 2021

5_Information error: Surveys - continuous variable as discrete

Information error - Implications of information error for surveys - cd

Video

edumedia@wur.nl

19 oktober 2021

Implication of information error for analytical studies (discrete variable)

Implication of information error for analytical studies (discrete variable), part of e-module theory information error.

Video

edumedia@wur.nl

19 oktober 2021

Implication of information error for analytical studies (continuous variable)

Implication of information error for analytical studies (continuous variable), part of e-module theory information error.

Video

edumedia@wur.nl

20 oktober 2021

8_Information error: analytical studies - continuous variable as discrete

Information error: analytical studies

Video

edumedia@wur.nl

19 oktober 2021

Implication of information error for surveys (categorized continuous variable)

Implication of information error for surveys (categorized continuous variable), part of e-module theory information error.

Video

edumedia@wur.nl

19 oktober 2021

Implication of information error for analytical studies (categorized continuous variable)

Implication of information error for analytical studies (categorized continuous variable), part of e-module theory information error.

Video

edumedia@wur.nl

8 mei 2018

09. Sampling- micro lecture

In this micro lecture the following items will be explained regarding this micro lecture: When do we need sampling? What does a sampling process look like? Two different types of sampling Probability sampling Non-probability sampling Criteria: Sampling bias & Sampling error Evaluating different types of sampling

Website

Henk van der Kolk

19 maart 2026

Testing

Main Concepts Technical / engineering testing: Checking whether the system or VR build works correctly, meets requirements, and behaves consistently. Research / participant testing: Testing with participants to verify that the experiment supports the hypothesis and produces valid data. Debugging: Systematically identifying, isolating, and fixing the cause of a technical problem. Learning Goals Distinguish between the two types of testing. Apply a simple technical testing protocol to a project. Systematically identify, document, and fix software or VR errors. Use the Testing Worksheet to maintain traceability and collaboration. Recognize testing as essential quality assurance for both research and development. Key Takeaways Technical testing validates the build before research testing validates the hypothesis. Debugging is guided observation — not random trial and error. Documenting your tests ensures accountability and repeatability. Continuous testing builds confidence and prevents surprises during research trials. A clear testing workflow saves time and makes collaboration smoother. Further Exploration Search for “Unity debugging and testing workflow.” Search for “How to create software testing protocols.” Search for “Difference between pilot study and technical testing.” Search for “Best practice bug tracking templates.”

Video

Lucia M Rabago Mayer, Jordi Weldink, Amanda Poulin