BIO401 Midterm Past Papers by Jawad Masroor

BIO401 Best File For MID Term Preparation

Define biostatistics:

The use of statistical methods to solve the biological problems. It is also called biometry.

Population and Sample:

Population refers to the entire group of individuals that share a common characteristic and are the focus of a study at a particular time. In population the measurable quantity is called parameter. While Sample is a subset of the population that is selected for the actual analysis. Sampling is often done when it is impossible to study the entire population. In sample the measurable quantity is called statistic.

Finite Population (Countable):

• The finite population is also known as a countable population in which the population can be counted.
• For statistical analysis, the finite population is more advantageous.
• Example: employees of a company.

Infinite Population (Uncountable):

• The population in which the counting of units

in the population is not possible.

• It is less advantageous in statistical analysis.
• Example: Number of germs in the patient’s body.

Parameter:

A parameter is a numerical characteristic of a population. It is a fixed value that describes some aspect of the population. For example, the average blood pressure of all adults in a specific region is a parameter.

Statistic:

A statistic is a numerical characteristic of a sample. It is used to estimate or infer the corresponding parameter of the population. For instance, the average blood pressure of a sample of 100 adults from the same region is a statistic.

Data:

Data refers to information collected, observed, or measured from population or sample during the study. This information can take various forms, including numerical values, categorical labels,

Course Name : Biostatistics · Course Code: BIO401 · Total Lectures : 88 · Mid Term Syllabus : Lecture No 01 to 44 · Final Term Syllabus : Lecture No 45 to 88

or descriptive details, and it serves as the foundation for statistical analysis. Biostatisticians use data to draw conclusions and derive meaningful insights about biological phenomena, health outcomes, or medical interventions. There are two types of data:

1. Primary Data:

• This refers to data that is collected firsthand directly from the source.
• Common methods of collecting primary data include surveys, interviews, experiments, and observations.
• Primary data is original, providing a high degree of relevance and specificity to the research question.
• It is expensive and take more time.

2. Secondary Data:

• The that has already been collected by someone else for a different purpose.
• Researchers utilize existing sources, such as published studies, government reports, or datasets, to obtain secondary data.
• Secondary data is less specific to the researcher's needs and may not perfectly align with the study objectives.
• It is not expensive and time-saving way to access a large volume of information.

Variable:

A variable is a characteristic that can be measured and that can assume different values. There are two types of variable:

1. Qualitative variable:

Qualitative variables are non-numeric and describe qualities or characteristics. Examples include gender, blood type, place of birth, ethnic group, type of drug etc.

2. Quantitative variable:

A quantitative variable is one that can be measured and expressed numerically. They have further two types:

a) Discrete Variable: Discrete variables are numeric variables that can only take specific, distinct values, often counted in whole numbers and cannot be subdivided into smaller units. Examples include the number of patients in a clinic, the number of red blood cells.

b) Continuous Variable: Continuous variables are numeric variables that can take any value within a range and can be measured with a high level of precision. These variables can be further subdivided into smaller and smaller units, and they are often associated with measurements rather than counts. Examples include height, weight, blood pressure, or temperature.

Sampling:

The process of selecting a subset from a larger population for study. There are two types:

1. Probability sampling:

In this type random selection take place and every element of the population has equal chance to be the part of selected sample. There many ways to do this:

a) Simple Random Sampling:

Every individual in the population has an equal chance of being selected, and each selection is independent of others.

b) Stratified Random Sampling:

The population is divided into subgroups or strata based on certain characteristics, and then random samples are taken from each stratum.

c) Cluster Sampling:

The population is divided into clusters, and then a random sample of clusters is selected. All members within the chosen clusters are included in the sample.

Stratified sampling, which involves selecting from every subgroup, cluster sampling involves selecting entire groups.

1. Systematic Sampling:

• A random starting point is selected, and then every kth element in the population is chosen for the sample.

e) Multistage Sampling:

Involves multiple stages of sampling. It often combines various sampling methods, such as cluster sampling followed by simple random sampling within selected clusters.

2. Non- Probability Sampling:

The sampling technique where not every member of the population has an equal chance of being included in the sample. Some common types of non-probability sampling methods include:

a) Convenience Sampling:

Participants are selected based on availability.

b) Purposive Sampling:

Choose based on the purpose of the study.

c) Quota Sampling:

Choose on the base of some pre-set standard. Proportion of the sample should be same as population.

d) Snowball Sampling:

First participants give the future participants. An initial set of participants is selected through purposeful sampling. These participants are then asked to refer others who might meet the study criteria.