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78 lines
3.5 KiB
Markdown
78 lines
3.5 KiB
Markdown
---
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title: "22-trends-in-hci"
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aliases:
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tags:
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- info203
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- lecture
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---
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[slides](https://blackboard.otago.ac.nz/bbcswebdav/pid-2827522-dt-content-rid-18612267_1/courses/INFO203_S1DNIE_2022/2022/INFO203_Lecture23.pdf
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how the the methodology of HCI used.)
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Theory vs practice. There is a lot of work being done t improve the methodology
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# Bad style - HARKing and the replication crisis
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# Publication bias
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- Computing research validate research claims using statistical significance as the standard of evidence
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- Statistical evidence usually assumes 95% confidence (p <= 0.05)
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- analysis of 362 papers published found that 97% reject the null
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- papers that were incorrecct are not published
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- *Publication bias*: Papers supporting their hypotheses are accepted for publication at a much higher rate than those that do not.
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- HARKing (Hypothesizing After the Results are Known) or known as ‘outcome switching’
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- Post-hoc reframing of experimental intentions to present a p-fished outcome as having been predicted from the start.
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- p-hacking: Manipulation of experimental and analysis methods to produce statistically significant results.
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- p-fishing: seeking statistically significant effects beyond the original hypothesis.
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A survey of over 2000 psychology researchers, John et al. examined the prevalence of questionable experimental practices (forms of HARKing):
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1. Failing to report all dependent measures, which opens the door for selective reporting of favourable findings – 63.4%;
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2. Deciding to collect additional data after checking if the effects were significant – 55.9%;
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3. Failing to report all of the study’s conditions – 27.7%;
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4. Stopping data collection early once the significant effect is found – 15.6%;
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5. Rounding off a p value (e.g., reporting p = .05 when the actual value is p = .054) – 22.0%;
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6. Selectively reporting studies that worked – 45.8%;
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7. Excluding data after looking at the impact of doing so – 38.2%;
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8. Reporting an unexpected finding as having been predicted – 27.0%;
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9. Reporting a lack of effect of demographic variables (e.g., gender) without checking – 3.0%;
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10. Falsifying data – 0.6%
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File drawer effect: Null findings tend to be unpublished and therefore hidden from the scientific community.
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## Solutions
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Preregistration: Registries in which researchers preregister their intentions, hypotheses, and methods (including sample sizes and precise plans for the data analyses) for upcoming experiments
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Preregistered Reports: Papers (Reports) are submitted for review prior to conducting the experiment. Registered reports include the study motivation, related work, hypotheses, and detailed method; everything that might be expected in a traditional paper except for the results and their interpretation.
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• Redefine or abdandon statistical significance. • Create data repositories and support replication
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# Trends
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## Wearable sensing and actuation
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applications
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- vr haptics
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- on the skin directions
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- notifications
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- more
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## electodermis
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focused more on the manufacturing of the stickers
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## earput
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[earput paper](https://i.imgur.com/ZqfaHUt.png)
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## skin track
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