# COURSE 2: Data Analysis Tools

Central Limit Theorem

As long as adequately large samples and an adequately large number of samples are used from a population, the distribution of the statistics will be normally distributed.

Hypothesis Testing

Definition: Assessing the evidence provided by the data, in favor of or against each hypothesis about the population.

Methods:

• ANOVA - Analysis of Variance
• X2 - Chi-Square of Independence
1. Specify the null($$h_0$$), and the alternate ($$h_a$$) hypothesis
2. Choose a sample
3. Assess the evidence
4. Draw conclusions

p value

Often noted as α, will be compared with “significance level of a test”, usually taken for 0.05. If p-value < α (0.05), the data provides significant evidence against the null hypothesis ($$H_0$$), so we reject the null hypothesis and accept the alternate hypothesis ($$H_a$$).

p value is also known as “Type One Error Rate”, means the number of times out of 100 we would be wrong if we reject the null hypothesis.

Bivariate Statistical tools

• ANOVA - Analysis of Variance
• X2 - Chi-Square of Independence
• r - Correlation Coefficient

How to choose a statistical test?

• C->Q: if you have categorical explanatory and quantitative response, choose ANOVA
• C->C: if you have categorical explanatory and response, choose X2
• Q->Q: if you have quantitative explanatory and response, choose Pearson Correlation
• Q->C: if you have categorical explanatory and quantitative response, you need to categorize your explanatory variable with only two levels then use the Chi-Square of Independence as your inferential test.