STATS

• Biostatistics


  It applies statistical methods to medical and biological problems
• Mathematical statistics


  Deals with the development of theories that serve as basis for statistical methods. It concerns the development of new methods of statistical inference and requires detailed knowledge of abstract mathematics for its implementation
• Applied statistics


  Refer to the application of statistical methods to solve real problems as well as the development of new statistical method motivated by real problems. It involves applying the methods of mathematical statistics to specific subject areas, such as economics, psychology, and public health
• Adolphe Quetelet


  • A Belgian astronomer and mathematician, who in his work combined the theory and practical methods of statistics and applied them to problems of biology, medicine, and sociology
• Francis Galton


  • The father of biostatistics and eugenics. His major contribution to biology was his application of statistical methodology to the analysis of biological variation, particularly through the analysis of variability and through his study of regression and correlation in biological measurements. His methodology has become the foundation for the application of statistics to biology
• Karl Pearson


  • Became interested in the application of statistical methods to biology, particularly in the demonstration of natural selection. Pearson's interest came about through the influence of W. F. R. Weldon, a zoologist at the same institution. Pearson continued in the tradition of Galton and laid the foundation for much of descriptive and correlational statistics. He is considered the founder of modern statistics
• Ronald Fisher


  • Invented experimental design and was one of the principal founders of population genetics. He unified the disconnected concepts of natural selection and Mendel's rules of inheritance
• Branches of Statistics


  • Descriptive Statistics
  • Inferential Statistics
• Descriptive Statistics


  • Tables/ Graphs
  • Measures of Central Tendency
  • Measures of Position
  • Measures of Variability
  • Measures of Correlation
• Inferential Statistics


  • Estimation
  • Hypothesis Testing
• Classification of Statistics


  • Parametric Statistics
  • Nonparametric Statistics
• Parametric Statistics

  • One – sample (z-test/t-test)
  • Two dependent samples (t-test)
  • Two independent samples (z-test)
  • > 2 independent samples (One-way ANOVA)
  • > 2 dependent samples (Two-way ANOVA)
• Nonparametric Statistics

  • One – sample (Sign test)
  • Two dependent samples (Wilcoxon Sign - Rank)
  • Two independent samples (Mann-Whitney)
  • > 2 independent samples (Kruskal-Wallis)
  • > 2 dependent samples (Friedman)
• Experimental design


  The master plan specifying the methods and procedures for collecting and analyzing the needed information
• Experimental design


  • Involves a series of decision-making choices to ensure the needed data can be gathered and analyzed
• Experiment


  Used to study causal relationships; you can manipulate one or more independent variables and measure their effect on one or more dependent variables
• Key steps in designing an experiment

  1. Consider your variables and how they are related

  2. Write a specific, testable hypothesis

  3. Design experimental treatments to manipulate your independent variable

  4. Assign subjects to groups, either between-subjects or within-subjects

  5. Plan how you will measure your dependent variable
• For valid conclusions, you also need to select a representative sample and control any extraneous variables that might influence your results
• If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead
• Types of research bias

  • Attrition bias
  • Non-response bias
  • Sampling bias
  • Survivorship bias
  • Undercoverage bias
• Extraneous variable


  Any factor that is not the independent variable that can affect an experiment's dependent variables
• Types of extraneous variables

  • Situational variables
  • Participant variables
  • Experimenter variables
  • Demand characteristic variables
• Confounding variable


  A type of extraneous variable that interferes directly with the outcome of a study
• Null hypothesis (H0)


  The hypothesis that the independent variable does not have an effect on the dependent variable
• Alternate hypothesis (H1)


  The hypothesis that the independent variable does have an effect on the dependent variable
• Experimental design types


  • Independent Measures Design (Between-groups)
  • Repeated Measures Design (Within-groups)
  • Matched Pairs
• Experimental research design types

  • Pre-experimental research design
  • True experimental research design
  • Quasi-experimental research design
• Characteristics of a good experimental design

  • Provides unbiased estimates of the factor effects and associated uncertainties
  • Enables the experimenter to detect important differences
  • Includes the plan for analysis and reporting of the results
  • Gives results that are easy to interpret
  • Permits conclusions that have wide validity
  • Shows the direction of better results
  • Is as simple as possible
• Descriptive statistics


  Specific methods basically used to calculate, describe, and summarize collected research data in a logical, meaningful, and efficient way
• Descriptive statistics


  It helps to describe and understand the features of a specific data set by giving short summaries about the sample and measures of the data
• Summary statistics


  Summarize and provide information about your collected data
• Summary statistics


  It tells you something about the values in your data set
• Summary statistics


  It could tell where your mean lies or whether your data is skewed
• Measures of central tendency

  • Mean
  • Median
  • Mode
  • Geometric Mean
• Measures of position


  • Deciles
  • Quartiles
  • Percentiles
• Measures of variability


  • Range
  • Interquartile Range
  • Quartile Deviation
  • Average Deviation
  • Standard Deviation
  • Variance
  • Coefficient of Variation
  • Standard Score
• Mean (ungrouped data)


  Sum of a collection of numbers divided by the count of numbers in the collection
• Geometric mean


  A special type of average where we multiply the numbers together and then take a square root (for two numbers), cube root (for three numbers)
• Median


  Middle observation/s that divides the observations into two equal parts of arranged observations from highest to lowest or vice versa
• Mode


  Most common occurring observation/s in the given set of data