Randomly collected samples don't necessarily create randomly shaped distributions. The central limit theorem shows you how the means of independently collected samples still create a normally distributed curve. These curves can then help us make inference
- [Instructor] Recall that the width…of the distribution of sample means…is affected by the sample size.…Why?…Because the standard deviation…is affected by the sample size.…There is a large kurtosis value…attributed to a smaller sample size.…And a smaller kurtosis value…associated with a larger sample size.…Thus, for larger samples the resulting distribution…of sample means will be more closely clustered…around the population mean.…In other words, the standard deviation…will generally be smaller for samples…that have a larger sample size,…which is something I've been kind of iterating all along.…
But there is something to keep in mind.…No matter what crazy shape…your population distribution takes, whether it's normal,…skewed one way or another, or just flat out strange,…if you have large enough samples,…your sample means distribution will always be normal.…And those means will be centered…around a true population mean.…Now very few researchers would actually…take thousands of samples, calculate all the sample means,…
- Quantitative vs. qualitative analysis
- Sample size considerations
- Normal distribution
- Estimating the population mean
- One-sample t-test
- Paired-sample t-test
- One-way and two-way ANOVA
- Repeated measure ANOVA
Skill Level Beginner
1. General Notions about Science and Research
2. Quantitative Research Fundamentals
3. One-Sample T-Test
4. Paired-Samples T-Test
5. Balanced One-Way ANOVA
6. Two-Way ANOVA
7. Repeated Measures ANOVA
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