Basic Statistics, Biostatistics, and Frequency Distribution
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The document is an introduction to biostatistics and research methodology, highlighting the importance of statistics in data collection, presentation, analysis, and interpretation across various fields such as healthcare and research. It covers key concepts including types of statistics (descriptive and inferential), the five stages of statistical investigation, and basic terms relevant to biostatistics, like populations, samples, and variables. Additionally, it discusses various scales of measurement and the organization of data through frequency distributions.
Basic Statistics, Biostatistics, and Frequency Distribution
- 1. Biostatistics and Research Methodology (BP801T) Introduction: Statistics, Biostatistics and Frequency Distribution Prepared and presented by: Mr. Gaurav S. Patil M. Pharm (Pharmaceutics)
- 2. Introduction to Biostatistics • Statistics is the science of collection, presentation, organization, summarization, analysis and interpretation of numerical data (raw material) from logical analysis. • According to Croxton and Cowden, there are four main components of statistics: Collection of data, Presentation of data, Analysis of Data, and Interpretation of Data. • Statisticians try to interpret and communicate the results to the wider groups. • The tools of statistics are employed in many fields: Business, education, economics, healthcare, industry, research etc.…
- 3. Introduction to Biostatistics Types of Statistics: • Descriptive statistics: A statistical method that is concerned with the collection, organization, summarization, and analysis of data from a sample of population. • Inferential statistics: A statistical method that is concerned with the drawing conclusions/infering about a particular population by selecting and measuring a random sample from the population.
- 4. Introduction to Biostatistics Importance of the statistics: • To able to conduct the research in effective manner. • Data analysis and interpretation help in making inform decision about in various fields. • To develop the critical and analytical research skill. • Crucial for research and scientific analysis • Supports risk assessment and decision-making in various domains
- 5. Introduction to Biostatistics There are five stages or steps in any statistical investigation: 1. Collection of Data: The process of obtaining measurements or counts. 2. Organization of Data: Includes editing, classifying, and tabulating the data collected. 3. Presentation of Data: Provides an overall view of what the data actually looks like. Can be done in the form of tables and graphs or diagrams. 4. Analysis of Data: Involves extracting relevant information from the data (e.g., mean, median, mode) 5. Interpretation of Data: Concerned with drawing conclusions from the data collected and analyzed. Gives meaning to the results of the analysis.
- 6. Introduction to Biostatistics Biostatistics • When the data analysed in statistics is derived from the bio- logical science and medicine, we use the term biostatistics. • Biostatistics greatly deals with the analysis and interpretation of the data based on biological sciences to draw certain implications. • It uses to define normal and health population, it helps to find and compare the efficiency of particular drug in human population, it help to identify the spread of diseases and etc…
- 7. Basic terms in Biostatistics • Population: is the complete set of possible measurements for which inferences/conclusions are to be made. • Sample: A sample from a population is the set of measurements that are actually collected in the course of an investigation. • Parameter: Characteristic or measure obtained from a population. E.g. Average weight of individual in sample. • Statistic: A statistic refers to a numerical quantity computed from sample data (e.g. the mean, the median, the maximum...).
- 8. Basic terms in Biostatistics • Variable: It is an item of interest that can take on many different numerical values. Or Any characteristic or quantity that can be measured or counted. E.g.: Height, weight • Data: Refers to a collection of facts, values, observations, or measurements that the variables can assume. • Sampling: The process or method of sample selection from the population. • Sample Size: The number of elements or observation to be included in the sample.
- 9. Entity Variables Values Person Age 0-150 years Gender Male, female Ethnicity Arab, Kurdish, Turkman, Yezidi, etc… Car Model Hyundai, KIA, BMW, Saipa Manufacture year 2010, 2015, 2021, etc… Engine V4, V6, V8 Color White, black, yellow, red, etc… Entities,Variables and Values Entity: something that exists separately from something else and has its own identity.
- 10. • Population of entities: the largest collection of entities for which we have an interest at a particular time. • Population of values: the largest collection of values of a variables. • Populations can be finite or infinite. • NOTE THAT in research, a population DOES NOT NECESSARILY refer to people. • A target population is the population under study. Populations
- 12. • A sample is a specific subgroup of a population that you will collect data from. • The size of a sample is always less than the total size of the population (otherwise it’s not a sample anymore). Samples
- 13. Variables • A variable is a characteristic that takes on different values in different persons, places, or things. • E.g.: • Heights of adult males. • Weights of preschool children. • Ages of patients seen in a dental clinic.
- 15. measured counted
- 16. Variables scale • Variable scale, in statistics, refers to the level of measurement or the nature of the data associated with a variable. • Nominal Scale: Nominal scales categorize data into distinct groups or categories without any inherent order. • Example: Colors (e.g., red, blue, green), gender (e.g., male, female), or types of fruits (e.g., apple, banana, orange). • Ordinal Scale: Ordinal scales not only categorize data but also indicate a meaningful order or rank among the categories. • Example: Education levels (e.g., high school, college, graduate),
- 17. Variables scale • Interval Scale: Interval scales have a consistent interval or distance between consecutive points, but they lack a true zero point. • Example: Temperature measured in Celsius or Fahrenheit (e.g., 20°C, 30°C, 40°C) • Ratio Scale: Ratio scales have a consistent interval, a meaningful zero point, and support all mathematical operations, including ratios. • Example: Height, weight, income - these variables have a true zero point (e.g., zero height, zero weight, zero income).
- 18. • Frequency distribution: A table or graph that shows how often different values or ranges of values occur in dataset. • Relative frequency distribution: Denoted/indicated frequencies as proportion or percentage of the total number of observations. • Cumulative frequency distribution: Cumulative frequency distribution is a form of frequency distribution that represents the sum of a class and all classes below it. • Relative cumulative frequency distribution: The cumulative relative frequency is the sum of all relative frequencies. Tabular Organization of Data
- 19. • Frequency Distribution: The number of observations falling into each class interval. • Relative Frequency Distribution: The proportion of observations in the particular class interval relative to the total observations. • Cumulative Frequency Distribution: Calculated by adding the number of observations in each class interval to the number of observations in the class interval above. • Cumulative Relative Frequency Distribution: Calculated by adding the relative frequency in each class interval to the relative frequency in the class interval above. Tabular Organization of Data
- 20. Tabular Organization of Data
- 21. Exercise This table shows the number of hours of 45 patients who slept following the administration of a certain anesthetic. Construct a table showing: 1. Frequency 2. Relativefrequency 3. Cumulative frequency 4. Cumulative relative frequency distribution.