Learn about key biostatistical concepts and efficient tools for summarising and plotting data as well as outlier detection. Demystify the statistical testing approach used to make decision in the presence of uncertainty: p-values, power, and so on.
- Why Do we Need Statistics?
- Descriptive or Exploratory Data Analysis "EDA"
- Overview and Goals
- Importance of Identifying the Type and Role of Variables in Studies
- Visualizing and Summarizing Data: The Concept of a Distribution
- Graphical Tools: histogram, Box-plot
- Numerical Tools: mean, median, standard deviation, standard error, etc.
- Exploring the relationship between two (2) variables
- Frequency tables for categorical variables
- Relative risks, odds ratios
- Pearson's correlation coefficient for continuous variables
- Plots: Scatter plots, box-plots, etc.
- Frequency tables for categorical variables
- Statistical inference or hypothesis testing
- Overview: What is statistical inference?
- Statistical Inference with Hypothesis Testing:
- Null and alternative hypotheses
- One-tailed vs. two-tailed tests
- Test statistics: t-test, F-tests, etc.
- Observed significance level or "p-value"
- Statistical significance and decision rules
- Risk involved in hypothesis testing
- Risks or type I and II errors
- Confidence level of a test
- Power of test
- The importance of sample size calculations and the required input parameters to estimate a sample size
- Statistical inference with confidence Intervals: interpretation and usage
- Statistical Inference for a Single Sample or Group: Hypothesis Testing vs. Confidence Interval Approach
- Statistical Inference for two Categorical Variables: The chi-square test
- Summary
This applied training session in statistics is aimed at all who collect data and who must make decisions based on that data.No formal knowledge of statistical tools is required to attend the class.
Upon completion of this course, participants will be able to:
- Understand the difference between descriptive and inferential statistics
- Appreciate the value of exploratory methods in preliminary data analysis and experimental design construction
- Explore, characterise and identify problems and trends in data using graphical tools
- Use descriptive statistics to summarise data
- Understand the concepts of hypothesis testing, confidence intervals, risk and power
- Identify the appropriate statistical test based on the study objective
- Understand the importance of sample size calculations and the required input parameters to estimate a sample size
- Analyse data more quickly and more accurately
- To interpret results reliably and confidently
Recommended Duration: 1 day(s)
Course Materials:
TBD
Case Studies:
N/A