This course offers an easy introduction to key concepts in statistics for people working in labs. Laboratory staff will learn how to efficiently visualise data, detect trends and outliers. They will also learn how to best summarise data. Statistical testing will be demystified: risks (alpha & beta), p-values, statistical significance, confidence and power are defined and illustrated. This course can serve as an introductory class or a refresher and provides a solid basis for all other courses.
- 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.
- Understanding Variability: Biological repetitions vs. analytical replicates
- Exploring the relationship between two (2) variables
- Frequency tables for categorical variables
- Pearson's correlation coefficient for continuous variables
- Plots: Scatter plots, box-plots, etc.
- 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
This applied training session in statistics is aimed at all who collect data and who must make decisions based on that data. This course introduces the important ideas in statistics and data analysis. It assumes that participants either have no previous knowledge of statistics or that they have not used statistics for a long time.
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, characterize and identify problems and trends in data using graphical tools
- Use descriptive statistics to summarize 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)
- Detailed Course Notes
- Provided by Instructor
- Client-Based Examples
This Session Has 3 Reviews
Formatrice expérimentée, Natalie a su rendre plus tangibles certaines notions de statistique essentielles mais plutôt méconnues. Elle a aussi relevé avec brio le défi de l’enseignement à distance. C’était une première expérience avec cette formatrice mais certe pas la dernière. Merci
Natalie gave us a distance learning course on statistics. She is very competent and clearly explains complex concepts, using some of our data that we provided as examples. Everyone in our group appreciated the training sessions and learned a lot. I strongly recommend Natalie’s services for statistics courses.
Natalie was a highly competent instructor and her course was quite relevant to our team’s needs. I recommend her program for those interested in acquiring basic and advanced knowledge on statistical analysis methods.
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