Introduction to biostatistics
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This document provides an introduction to biostatistics concepts. It discusses populations and samples, and how to obtain a representative sample through probability sampling techniques. It also covers levels of variable measurement, classifications of variables, reliability vs. validity, and descriptive and inferential statistical techniques. The goal is to describe key biostatistics terms and how they are used in quantitative research.
Introduction to biostatistics
- 1. Introduction to Biostatistics Dr. Weam Banjar, DDS., MSc., Comp.Dent Fellowship
- 2. Population vs. Sample: Population 7/18/2018 Introduction to Biostatistics-WMB 2
- 3. Population vs. Sample: PopulationTarget population 7/18/2018 Introduction to Biostatistics-WMB 3
- 4. Population vs. Sample: PopulationTarget population Accessible population 7/18/2018 Introduction to Biostatistics-WMB 4
- 5. Population vs. Sample: PopulationTarget population Accessible populationSample 7/18/2018 Introduction to Biostatistics-WMB 5
- 6. Population vs. Sample: PopulationTarget population Accessible populationSampleRepresentative Sample 7/18/2018 Introduction to Biostatistics-WMB 6
- 7. Achieving representative sample: Representative sample Probability sampling technique Homogenous population Large Sample size (power analysis) 7/18/2018 Introduction to Biostatistics-WMB 7
- 8. Strata: 7/18/2018 Introduction to Biostatistics-WMB 8
- 9. Sampling bias: 7/18/2018 Introduction to Biostatistics-WMB 9
- 10. Sampling Techniques: Non-probability sampling Probability Sampling • Does not involve selection of elements at random • Rarely representative of the population • has an equal, independent chance of being selected. • Allows researchers to estimate the magnitude of sampling error (difference between population values and sample values) 7/18/2018 Introduction to Biostatistics-WMB 10
- 11. Types of non-probability sampling technique: • Convenience sampling: selecting the most conveniently available people as participants • Quota sampling: identifying population strata and figuring out how many people are needed from each stratum • Consecutive sampling: recruiting all people from an accessible population over a specific time interval • Purposive sampling: handpicking sample members 7/18/2018 Introduction to Biostatistics-WMB 11
- 12. Types of probability sampling technique: • Simple random sampling: Completely random method for selecting subjects • Stratified random sampling: Involves splitting subjects into mutually exclusive groups then using use simple random method for selection • Systematic sampling: To choose every (nth) participant from a complete list • Cluster random sampling: To randomly select participants from a list that is too large for simple random sampling • Multistage random sampling: Combination of techniques 7/18/2018 Introduction to Biostatistics-WMB 12
- 13. Level of variables measurements: • Nominal: categorical, two or more, no order. Example: race • Ordinal: can be ranked in order, interval between values can not interpreted. Example: pain score • Ratio: numerical, zero is meaningful. Example: age, height • Intervals: distance between attributes had meaning, interval between values could be interpreted. Example: age groups 7/18/2018 Introduction to Biostatistics-WMB 13
- 14. Classification of variables: • Dependent variable : Outcome of interest (what do you intend to measure?) • Independent variable: Exposure of interest (what are you going to investigate?) • Confounder: variables that might influence the results other than independent and dependent variable 7/18/2018 Introduction to Biostatistics-WMB 14
- 15. Reliability vs validity: Reliability Validity • Refers to whether a method or measurement will repeatedly give the same result if used by the same person more than once • Implies consistency • Refers to the ability of methodology to measure what it is supposed to measure • Implies accurance 7/18/2018 Introduction to Biostatistics-WMB 15
- 16. Generalizability: an important feature of quantitative research because it allows the researcher to have a fair degree of certainty that the findings of the research apply to people that have the same, or broadly similar, characteristics as the people involved in the study. 7/18/2018 Introduction to Biostatistics-WMB 16
- 17. Basic statistical techniques: Descriptive statistics Inferential statistics • Used to describe data • 1st step in any statistical treatment • Uses measures of central tendency and variability (standard deviation) • Provide an objective way of quantifying the strength of evidence for the hypothesis • To demonstrate that the findings extend beyond the sample 7/18/2018 Introduction to Biostatistics-WMB 17
- 18. Descriptive statistics: Measures of central tendency Measures of dispersion • Mean= Average • Median= middle value in an ordered set of values • Mode= most frequently occurring number in a set of observations • Range: difference between largest and smallest observations • Standard deviation (sample): • Standard error (population) 7/18/2018 Introduction to Biostatistics-WMB 18
- 19. Measures of central tendencies that could be used for specific level of measurements : Mean Median Mode Nominal Ordinal X X Interval/Ratio X X X 7/18/2018 Introduction to Biostatistics-WMB 19
- 20. Inferential statistics: • indicate that the statistical findings are subject to some presumption on the part of the person doing the statistics. • Presented as: Probabilities (p values) Remain open to proved or disproved • A p value of 0.1 (usually written as p=0.1) would mean that there is a 10% chance that the findings of the study occurred by chance. • The lower the number the better (The lower the P value, the higher the power) 7/18/2018 Introduction to Biostatistics-WMB 20
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