Week 1

Learning Objectives

1. Identify the key stages of the statistical research process and explain the purpose of each stage 

2. Distinguish between population and sample, and between parameter and statistic, using correct terminology 

3. Interpret means and proportions reported in published abstracts by identifying the population (sample and target), parameter being estimated, and sample statistic calculated

4. Explain why the same measurement on different samples produces different statistics, using concepts of random variable, distribution, and sampling variability

5. Identify the parameters and symmetric nature of the normal distribution

6. Evaluate whether conclusions from published studies about study populations generalize to target populations 

Vocabulary Terms (Layman's Terms):

• Population 

  • Target population - The population you would like to make claims about

  • Study population - The population that the study design can support

  • Parameter - The quantity (number) you are interested in studying 

• Sample

  • Simple random sample - All individuals have equal chance of being selected

  • Statistic - The quantity (number) you calculate from a sample

  • Sampling variability - Sample statistics (like sample means) will vary between samples (why? individuals differ and it just depends who ends up in your sample)

  • Repeated sampling - Repeating (i.e., "replicating") a study over and over through resampling and calculating a statistic

  • Sampling distribution - The pattern that describes how sample statistics are spread out or concentrated over repeated sampling

    • Bias - A sample statistic is biased if on average across repeated 

      • Positively Biased - The sample statistic as an estimate of the parameter will overestimate a parameter, on average over repeated samples

      • Negatively Biased - The sample statistic will underestimate a parameter, on average over repeated samples

    • Unbiased - The sample statistic will replicate the parameter, on average over repeated samples

• Random variable

  • Distribution - The pattern that describes how any random variable is spread out or concentrated

    • Symmetric 

    • Skewed

    • Multimodal