Data in Biostatisttics.,,.....,......pptx

Biostat

1. Tribute Sir Ronald Aylmer Fisher  (1890-1962) is known as the "Father of Modern Statistics". He was a British statistician, biologist, geneticist, and mathematician who made significant contributions to the field of statistics .

2. Statistics Statistics is the science of collecting, organizing, analyzing, interpreting, and presenting data. It provides methods to make informed decisions based on data. Statistics is used in a wide variety of fields, such as business, healthcare, economics, and social sciences. Examples of Statistics Descriptive Statistics : Summarizing a dataset with measures such as mean, median, mode, standard deviation, and graphs. Example: Calculating the average income of people in a city. Inferential Statistics : Drawing conclusions or making predictions about a population based on sample data. Example: Predicting election results by analyzing survey data from a subset of voters.

3. Biostatistics It is derived from the Greek word Biometry : Bios (Life) + Metron (Measure). Biostatistics is a branch of statistics that focuses on applying statistical techniques to biological, health, and medical research. It helps analyze data from clinical trials, epidemiological studies, and public health interventions. Examples of Biostatistics Epidemiology : Estimating the incidence and prevalence of diseases. Example: Measuring the percentage of the population affected by diabetes in Bangladesh and analyzing its trends over the years. Clinical Trials : Testing the efficacy of a new drug or treatment. Example: Using biostatistical methods to determine if a new vaccine reduces the risk of infection compared to a placebo group. Public Health : Evaluating health interventions. Example: Assessing the impact of a smoking cessation program on reducing lung cancer rates in a community.

4. Use/Function of Biostatistics: Collection of information Simplification of huge and complex set of data. Elucidation of ideas. Community health diagnosis-identification of health problems. Measurement of association between two or more variables. Prediction and forecasting. Designing experimental ideas. Analyzing data and drawing conclusion. Providing information support for planning, monitoring, evaluating and managing health service programs.

5. Data and Its Types The raw material of statistics is data. Data refers to facts, figures, measurements, or observations collected from a study, survey, or experiment. In biostatistics, data are numerical or categorical values derived from biological, medical, or public health research. These values help in analyzing and interpreting health-related phenomena. Key Points Quantitative data answers "how much?" or "how many?" Qualitative data answers "what type?" or "what category?"

6. Quantitative data represents numerical values that can be measured or counted. Discrete Data : Represents countable, finite values (whole numbers). Examples : Number of patients in a hospital. Number of children in a family. Number of cars in a parking lot. Continuous Data : Represents measurable values that can take any value within a range. Examples : Height of a person (e.g., 170.5 cm). Weight of a patient (e.g., 68.2 kg). Blood pressure (e.g., 120/80 mmHg). Quantitative Data (Numerical Data)

7. Qualitative Data (Categorical Data) Qualitative data represents characteristics, categories, or labels. These data describe qualities rather than measurements. Nominal Data : Categories without a natural order or ranking. Data that can name. They are not measured but simply counted. Examples : Blood groups (A, B, AB, O). Eye color (Brown, Blue, Green). Gender (Male, Female). Cured or not cured. Death or alive Ordinal Data : Categories with a meaningful order or ranking, but the differences between them are not measurable. Examples : Pain severity (Mild, Moderate, Severe). Education level (Primary, Secondary, Tertiary). Satisfaction level (Satisfied, Neutral, Dissatisfied).

8. Aspect Quantitative Data Qualitative Data Definition Represents numerical values that can be measured or counted. Represents descriptive attributes, labels, or categories. Nature Numerical Categorical Measurement Uses numbers and mathematical operations (e.g., addition, averages). Uses labels, names, or descriptive classifications. Subtypes - Discrete (countable whole numbers) - Continuous (measurable, any value in a range) - Nominal (no order) - Ordinal (ordered categories) Examples - Age (e.g., 25 years) - Weight (e.g., 68.5 kg) - Blood pressure (e.g., 120/80 mmHg) - Gender (Male, Female) - Blood groups (A, B, AB, O) - Pain level (Mild, Moderate, Severe) Analysis Analyzed using statistical techniques like mean, standard deviation, regression. Analyzed using frequency, proportions, or non-parametric tests. Graphical Representation Represented using histograms, line graphs, scatter plots. Represented using bar charts, pie charts. Data Collection Collected through instruments like scales, thermometers, surveys. Collected through interviews, observations, or open-ended questions. Usage Used to measure or compare values. Used to classify or describe characteristics.

9. Relationship Between Data and Variable Variable : A concept or property you are interested in studying (e.g., height, blood pressure, smoking status). Data : The values or observations you collect for that variable (e.g., height = 160 cm, blood pressure = 120/80 mmHg, smoking status = Smoker).

10. Examples in Biostatistics Variable : Blood Pressure Data : 120/80 mmHg, 130/85 mmHg, 140/90 mmHg Variable : Disease Status Data : Present, Absent