Foundations of Inference in Python
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Run and interpret SciPy hypothesis tests, effect sizes, power checks, bootstrapping and permutation tests to draw sound conclusions from data in Python.
Statistical inference in Python turns averages and plots into decisions that hold up. You draw samples and see how a poor one skews the result. You choose SciPy tests for normality, correlation and group differences, then judge how strong an effect is and whether a study has enough power. Simulation methods such as bootstrapping and permutation tests, along with combining p-values across studies, extend the toolkit to skewed data and to other researchers’ results.
What You Will Achieve
- Build confidence intervals from a sample and spot when sampling bias distorts the conclusion
- Check whether data are normal or correlated in SciPy, then pick a parametric or non-parametric test to match
- Report how strong an effect is, adjust p-values when running many tests, and work out the sample size a study needs
- Reach conclusions on skewed data or several studies at once with resampling methods and combined p-values
Program Overview
Sampling and Confidence Intervals
How the choice of a sample shapes what you can conclude, and how to put a range around an estimate.
- Random sampling and point estimates
- Sampling bias and visualizing samples
- Normal sampling distributions
- Calculating confidence intervals
Hypothesis Tests in SciPy
Picking the right test for the data and reading its output for a decision.
- Normality tests and fitting a normal distribution
- Correlation, autocorrelation and explained variance
- Equal variance checks and ANOVA
- Non-parametric tests for rankings and medians
Effect Size, Power, and Simulation
Measuring the strength of a result, guarding against spurious findings, and drawing on resampling and combined evidence.
- Effect size for means, correlations and categorical variables
- Multiple comparisons and corrections
- Power of a test and sample sizes
- Bootstrapping, permutation tests and Fisher’s method
Certificate Awarded (Paid)
Complete the course to earn it.
Job Outlook
These skills apply in data analysis, data science and business analysis work, wherever sampled data or experiment results have to support a decision that others will rely on.
Good for: Data analysts who want their Python test results backed by effect sizes, power checks and resampling before those results inform a decision.
Keep in mind: Despite its advanced label, the course is aimed at intermediate learners, and all of its tests run in Python with SciPy.
Bottom line: It covers the checks that separate a significant p-value from a result worth acting on, with bootstrapping and Fisher’s method included.
Frequently Asked Questions
Which Python library runs the statistical tests?
SciPy is used throughout. You run normality, correlation, ANOVA and non-parametric tests with it, and you apply Fisher's method for combining p-values with SciPy as well.
Why measure effect size when a test is already significant?
A p-value shows whether an effect is likely present, but not how strong it is. Effect size measures the strength of a treatment's effect, and the lessons cover it for means, correlations and categorical variables.
How are results from several studies combined?
A lesson on meta-analysis uses Fisher's method to combine evidence from the p-values of multiple studies, which lets you re-analyze and pool results reported by other researchers.
When do permutation tests come into play?
Permutation tests close the final chapter as a flexible non-parametric option. You use them for correlations, compare them with bootstrapping, and apply one to analyze skewed data.
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