Introduction of BiostaticsZaira SolomanPost RN (BScN), RM(Lecturer)**

Objective After the end of this lecture you all will be able to:Define statistics and biostatistics.Know the history of biostatistics.Differentiates its type.Understand the limitation of biostatistics.Describe the aims of biostatistics.Learn the statistical terms

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Statistics It is the study of methods and procedures for collection, classification, analysis and interpretation of data to make scientific inference from it.The term has been derived from the Latin word status, the Italian word statistica and the German word statistik.**

Biostatistics and BiometryBiostatistics is the application of statistical methods to the problem of biology, including human biology, medicine and public health.

Statistical methods including the Collection

Organization**

ContiSummary-classification

Analyzing and measurements of facts to reach some inference.

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Biostatistics also called BIOMETRY (literally mean biological measurement)

The word biometry has Greek origin ( bios means life and metron means measured).**Conti

Brief History1)Adolph Quetelet (1796-1874) A Belgian mathematician, is thought to use the statistical methods for the 1st time in his work and applied them to the problem of biology, medicine & sociology.

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2) Francis Galton (1822-1911)A cousin of Charles Darwin is called the Father of Biostatistics and Eugenics he made notable contribute in the field of heredity

3. Karl Pearson (1857-1936)applied Statistical methods in the demonstration of natural selection and laid the foundation for descriptive and correlation statistics.

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4) W.F.R Weldon (1860-1906) A zoologist at university Collage, London. The Term biometry was coined by him.

5) Ronald A. Fisher(1890-1962) The dominant figure in statistics and biometry in 19th century has been done by him. His major contribution is small sample theory are used in almost all the fields of science.**

Types of BiostaticsBiostatistics can be divided into two subcategories.

Descriptive BiostatisticsInferential Biostatics**

1. Descriptive Biostatistics It is the study of biostatistics procedure which deal with the CollectionRepresentationThe summarizing of data to make it more informative and comprehensible.

It involves graphical and tabular approaches to describe, summarize and analyze the data.**

Conti..The primary function of descriptive statistics is to provide meaningful and convenient techniques for describing feature of data that are of interest.The failure to choose appropriate descriptive statistics often lead to faulty scientific inference.**

Conti..The field of descriptive statistics is not concerned with the conclusions that can be drawn from the set of data.It is basically a device for organizing data and bringing into focus their essential characters for the purpose of conclusion.

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2. Inferential Biostatics

Process of drawing information from sampled observations of a population and making conclusions about the population. Inferential statistics have a two-prong approach. First, sampling must be conducted to be representative of the underlying population

Second, the procedures must be capable of drawing correct conclusions about the population.

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Limitations of the biostatistical methodsIt can not be applied to all kind of phenomena.Statistical lows are not exact law like mathematical or chemical.Statistical data can be treated as approximations or as estimation, not as precise measurement.Statistical techniques deals with the quantitative data only.**

Conti.The technique is same for the social as well as physical science.Its only a tool and not an end itself.The greatest limitation of statistic is that only one who has a sound knowledge off statistical methods can efficiently handle statistical data.Some errors are possible in statistical decisions.**

Aims of biostatisticsBiostatistics is basically concerned with three purposes:To generate the statistical data through experimental investigation and sample survey.To organize and represent the data in suitable tables, diagrams, charts or graph, etc.To draw valid inferences from the data collected, predict the future outcomes from the data.

STATISTICAL TERMS**

1. Population:Ordinarily, the word population is used to mean the number of people living in a area, a region or a country.But in statistical investigation, population refers to any well defined group of individual who are being studied of observation of a particular type.

E.g.All students in LNH could be a population.All patient of a hospital suffering from Hepatitis and treated with a new drug may be considered as population.

2. SampleA sample is a group of units selected from a larger group. A sample is generally selected for study because the population is too large to study in its entirety. The sample should be representative of the general population.Example The population for a study of infant health might be all children born in the UK in the 1980's. The sample might be all babies born on 7th May in any of the years.**

Type of Sampling:Random sample: It gives every number of the population an equal chance of being selected. No one in the population is favored over the other in the selection process.

Biased Sample or Nonrandom Sample: Does not provide equal opportunity for all members of the population of being selected. Sample is drawn for a purpose.

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3. Unit A unit in a statistical analysis refers to one member of a set of entities being studied.Unit is the smallest object or individual that can be investigated as the source of basic information, e.g. small sub areas of land. individual patient, etc. units are expressed in two ways:Samplings unite: During survey.Experimental units : During experiment.**

4. BiasIs a systematic (built-in) error which makes all measurements wrong by a certain amount.

Examples of BiasThe scales read "1 kg" when there is nothing on them You always measure your height wearing shoes with thick soles. A stopwatch that takes half a second to stop when clicked

5. AccuracyAccuracy is how close a measured value is to the actual (true) value.

6. Precision Precision is how close the measured values are to each other. (If you measure something several times and all values are close, they may all be wrong if there is a "Bias)

Difference & Similarities

Accuracy / Precision

Example One can say that a measurement is accurate but not precise; precise but not accurate; neither or both. An example of bad precision and good accuracy can be: Suppose a lab refrigerator holds a constant temperature of 38.0 F. A temperature sensor is tested 10 times in the refrigerator. The temperatures from the test yield the temperatures of: 37.8, 38.3, 38.1, 38.0, 37.6, 38.2, 38.0, 38.0, 37.4, 38.3. This distribution shows no impressive tendency toward a particular value (lack of precision) but each value does come close to the actual temperature (high accuracy).

7. Date:Data is a set of facts expressed in quantitative form. It can be primary or secondary.The data collected by investigator from personal experience is called primary data.

While the data obtained from some secondary source such as journals, magazines, news paper, etc.is known as secondary data.

Univariate vs. Bivariate DataStatistical data are often classified according to the number of variables being studied.

Univariate data. When we conduct a study that looks at only one variable, we say that we are working with univariate data. Suppose, for example, that we conducted a survey to estimate the average weight of high school students. Since we are only working with one variable (weight), we would be working with univariate data. **

ContiBivariate data. When we conduct a study that examines the relationship between two variables, we are working with Bivariate data. Suppose we conducted a study to see if there were a relationship between the height and weight of high school students. Since we are working with two variables (height and weight), we would be working with Bivariate data.

8.Parameter A parameter is a value, usually unknown (and which therefore has to be estimated), used to represent a certain population characteristic. For example, the population mean is a parameter that is often used to indicate the average value of a quantity.Within a population, a parameter is a fixed value which does not vary. **

Conti.Each sample drawn from the population has its own value of any statistic that is used to estimate this parameter. For example, the mean of the data in a sample is used to give information about the overall mean in the population from which that sample was drawn.

Thank you

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*Eugenicsis theapplied scienceof the bio-social movementwhich advocates the use of practices aimed at improving thegenetic composition of a population, usually a human population.[*natural selection The process whereby organisms better adapted to their environment tend to survive and produce more offspring.* used in almost all **