text: # 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.
explanation: The provided text is a course excerpt on data analysis tools, covering topics such as the Central Limit Theorem, Hypothesis Testing, p-values, and Bivariate Statistical tools. It includes definitions, methods, and guidelines for choosing statistical tests based on the types of variables involved. The text does not contain a specific question or a question-and-answer pair.
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distilabel_metadata.role: system
distilabel_metadata.content: # 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.
distilabel_metadata.role: user
distilabel_metadata.raw_output_q_a_detector_0: The provided text is a course excerpt on data analysis tools, covering topics such as the Central Limit Theorem, Hypothesis Testing, p-values, and Bivariate Statistical tools. It includes definitions, methods, and guidelines for choosing statistical tests based on the types of variables involved. The text does not contain a specific question or a question-and-answer pair.
  
  Label: no-question