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Foundations of Inference in R
4.8(53)38k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate R course demystifies classical statistical inference by building intuition around null hypotheses, sample variation, and population estimation. Through hands-on exercises, learners build a conceptual and practical understanding of p-values and confidence intervals to quantify uncertainties in data-driven conclusions.
Who It's For
R users, academic researchers, and analysts who want to move beyond descriptive statistics to make valid population inferences.
Key Takeaways
- Statistical Hypothesis Logic: Understand how null hypothesis testing is used to draw valid conclusions about larger populations.
- p-Value Interpretation: Learn to calculate and interpret p-values to evaluate evidence against baseline assumptions.
- Confidence Intervals: Construct confidence intervals to measure effect size magnitudes and quantify estimation uncertainty.
About this course
One of the foundational aspects of statistical analysis is inference, or the process of drawing conclusions about a larger population from a sample of data. Although counter intuitive, the standard practice is to attempt to disprove a research claim that is not of interest. For example, to show that one medical treatment is better than another, we can assume that the two treatments lead to equal survival rates only to then be disproved by the data. Additionally, we introduce the idea of a p-value, or the degree of disagreement between the data and the hypothesis. We also dive into confidence intervals, which measure the magnitude of the effect of interest (e.g. how much better one treatment is than another).
Price
Free
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