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HYPOTHESIS TESTING & CONFIDENCE INTERVALS : The statistical techniques are generally aimed at helping us understand population parameters.
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MACHINE LEARNING : Techniques aimed at helping us draw conclusions about individuals in our population.
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BONFERRONI CORRECTION : Method for correcting type I error threshold when we perform more than one hypothesis test.
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With larger sample sizes, STATISTICAL SIGNIFICANCE becomes less relevant
Descriptive statistics is about describing our collected data using the measures discussed throughout this lesson: measures of center, measures of spread, shape of our distribution, and outliers. We can also use plots of our data to gain a better understanding.
Inferential Statistics is about using our collected data to draw conclusions to a larger population. Performing inferential statistics well requires that we take a sample that accurately represents our population of interest.
Important terms-
- Population - our entire group of interest.
- Parameter - numeric summary about a population
- Sample - subset of the population
- Statistic numeric summary about a sample
Baye's Diag.
Robot sensing and Baye's Theorem
Common hypothesis tests include:
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Testing a population mean One sample t-test
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Testing the difference in means Two sample t-test
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Testing the difference before and after some treatment on the same individual Paired t-test
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Testing a population proportion One sample z-test
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Testing the difference between population proportions Two sample z-test