What is the value of Science? In this course we will discuss why do we need Science, how Science improves people’s lives and how to do it better.
Here you find the slides that were used in classes.
- Class 1: The Scientific Method. (Feb 14, 2020). An operational definition of Science. [Slides].
- Class 2: Where is the Truth?. (Feb 21, 2020). How do we know what we know. [Slides].
- Class 3: Experiments, Scientific Impact and the Meaning of Life. (Feb 26, 2020). Motivation of the course [Slides].
- Class 4: Observing and Measuring. (Feb 28, 2020). What can be measured. Buildings, barometers and aliens. Uncertainty of measurements. [Slides].
- Class 5: Uncertainty of measurements. (Mar 4, 2020). Handling uncertainty in measurements. Propagation of uncertainty [Slides].
- Class 6: Statistical error. (Mar 11, 2020). Other sources of uncertainty in measurements. Advanced propagation of uncertainty. [Slides].
- Recommended Podcast (Feb 23, 2020).
- Estimating Statistical Uncertainty (Mar 25, 2020).
Every time we measure, we get a different number. How can we extract meaningful information from noisy data? How much information can we extract? Classical statistics gives an answer to these questions.
- Application: Evaluating Statistical Uncertainty (Mar 26, 2020).
Measuring the same value several times may give different results. The real value is somewhere in a confidence interval. To find such interval we need to evaluate the statistical uncertainty.
- Uncertainty in Linear Models (Apr 1, 2020).
When we can assume that real values follow a straight line, we can reduce the uncertainty. Thus, we can predict the number of COVID-19 cases, and see if health-care decisions are effective or not.
- Experiment: measuring the speed of sound (Apr 8, 2020).
It is not hard or expensive to measure the speed of sound. But, what are the margins of error?
- Application: Uncertainty in Linear models (Apr 9, 2020).
Beyond making predictions, linear models allow us to measure values that are hidden under a mountain of data. Learn how to measure these values and determine their confidence intervals.
- Linear models with categorical factors (Apr 24, 2020).
Linear models allow us to predict from experimental data, and define confidence intervals for these predictions. Moreover, the coefficients of the linear model reveal useful information, with their corresponding confidence intervals. In this article we explore the case when some of the independent variables are not numeric but instead are in a nominal scale.
- Linear models for microarray analysis (May 9, 2020).
A gene expression experiment measures messenger RNA concentrations under a specific growth condition. We would like to know how does a gene concentration change when conditions change. But our measurements mix real gene expression and noise. How can we find separate noise and signal?
- Methodology of Scientific Research (Mar 8, 2021).
- Why Science matters
- What is Science, and what is not
- How do we know what we know
- What cannot be known
- What is the relationship between Science and Technology
- How Technology changes Science
- What are the key elements of good world-class Science
- The role of ideas, models and abstractions
- The role of experiments, tools and instruments
- What makes a good scientific explanation
- How not to fool ourselves
- How to do science with high impact