Discussion for (Un)certainty of Science by SEPA강남미래인재교육원 https://padlet.com/snusepa/isec240626 ISEC Workshop (jinwoong song) en-us 2024-06-25 23:49:21 UTC 2024-06-26 03:12:00 UTC hello@padlet.com https://padlet.net/icons/8.0/png/1f52c.png Type of Uncertainty snusepa https://padlet.com/snusepa/isec240626/wish/3037997393 1. Data Sampling and Measurement

  • It occurs due to extraction of only a part of nature as data and limitations of measurement devices.

2. Theory and Model Generation

  • It occurs due to theories and models undergo idealization processes and may be subject to modification.

3. Prediction and Decision-making

  • Predicting the future perfectly through science is impossible. It occurs because especially when inter-wined with society.

4. Human activity

  • It occurs as scientists are also human and work together and interact each other as a member of the professional community.


]]> 2024-06-26 00:50:28 UTC https://padlet.com/snusepa/isec240626/wish/3037997393 Measuring tool https://padlet.com/snusepa/isec240626/wish/3038185766 The tool for measuring the air pollution level can make the uncertainties of data.

]]> 2024-06-26 03:01:23 UTC https://padlet.com/snusepa/isec240626/wish/3038185766 https://padlet.com/snusepa/isec240626/wish/3038186312

  • Data Sampling and measurement

    • inherent uncertainty in sensor

    • sensor measures in real time, uncertainty in deciding which value to record

  • Human activity

    • Random error due to human errror/variability

    • ]]>     2024-06-26 03:01:43 UTC https://padlet.com/snusepa/isec240626/wish/3038186312     Collection of data is limited. Air samples are not collected over a wide spread of days. There could be sampling bias. martinfarrell1 https://padlet.com/snusepa/isec240626/wish/3038194398 Quality of instrument is not known.



    ]]>     2024-06-26 03:06:39 UTC https://padlet.com/snusepa/isec240626/wish/3038194398     challenges https://padlet.com/snusepa/isec240626/wish/3038194958 1. Measurement : areas (outside and inside)/time for data collection/ tool

    1. Model generation : different graph based on different data

    2. Interpretation: focus area/criteria for interpretation

    3. Prediction: focus area/prior experience

    4. Decision-making : focus area which we want to solve and past experience


    Bias / Prior experience : the difference between evidence and personal idea

    ]]>     2024-06-26 03:07:02 UTC https://padlet.com/snusepa/isec240626/wish/3038194958     https://padlet.com/snusepa/isec240626/wish/3038200252 Limitations and challenges in:

    • Collection of data

    • Instrument tool

    • Planning, data collection, data analysis and discussion



    ]]>     2024-06-26 03:10:22 UTC https://padlet.com/snusepa/isec240626/wish/3038200252