---
title: "22-trends-in-hci"
aliases: 
tags: 
- info203
- lecture
---

[slides](https://blackboard.otago.ac.nz/bbcswebdav/pid-2827522-dt-content-rid-18612267_1/courses/INFO203_S1DNIE_2022/2022/INFO203_Lecture23.pdf
how the the methodology of HCI used.)

Theory vs practice. There is a lot of work being done t improve the methodology

# Bad style - HARKing and the replication crisis

![scienfific process](https://i.imgur.com/m1HDqN3.png)

# Publication bias
- Computing research validate research claims using statistical significance as the standard of evidence 
- Statistical evidence usually assumes 95% confidence (p <= 0.05)
- analysis of 362 papers published found that 97% reject the null
- papers that were incorrecct are not published
- *Publication bias*: Papers supporting their hypotheses are accepted for publication at a much higher rate than those that do not.
- HARKing (Hypothesizing After the Results are Known) or known as ‘outcome switching’
	- Post-hoc reframing of experimental intentions to present a p-fished outcome as having been predicted from the start.
- p-hacking: Manipulation of experimental and analysis methods to produce statistically significant results. 
- p-fishing: seeking statistically significant effects beyond the original hypothesis.

A survey of over 2000 psychology researchers, John et al. examined the prevalence of questionable experimental practices (forms of HARKing): 
1. Failing to report all dependent measures, which opens the door for selective reporting of favourable findings – 63.4%; 
2. Deciding to collect additional data after checking if the effects were significant – 55.9%; 
3. Failing to report all of the study’s conditions – 27.7%; 
4. Stopping data collection early once the significant effect is found – 15.6%; 
5. Rounding off a p value (e.g., reporting p = .05 when the actual value is p = .054) – 22.0%; 
6. Selectively reporting studies that worked – 45.8%; 
7. Excluding data after looking at the impact of doing so – 38.2%; 
8. Reporting an unexpected finding as having been predicted – 27.0%; 
9. Reporting a lack of effect of demographic variables (e.g., gender) without checking – 3.0%;
10. Falsifying data – 0.6%

![](https://i.imgur.com/gro2KnF.png)

File drawer effect: Null findings tend to be unpublished and therefore hidden from the scientific community.

## Solutions

Preregistration: Registries in which researchers preregister their intentions, hypotheses, and methods (including sample sizes and precise plans for the data analyses) for upcoming experiments

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.

• Redefine or abdandon statistical significance. • Create data repositories and support replication

# Trends
## Wearable sensing and actuation
![springlets paper](https://i.imgur.com/QQMmEAk.png)

![springlets prototypes](https://i.imgur.com/zbRefG0.png)
![more prototypes](https://i.imgur.com/dVmrJf9.png)

applications
- vr haptics
- on the skin directions
- notifications
- more

![evaluation](https://i.imgur.com/4edmUVX.png)

## electodermis
![electro dermis paper](https://i.imgur.com/9MMLpOG.png)
focused more on the manufacturing of the stickers

## earput
[earput paper](https://i.imgur.com/ZqfaHUt.png)
![earput evaluaion](https://i.imgur.com/97ZAqGv.png)

## skin track
![](https://i.imgur.com/LlfFZki.png)
