Introduction
Understanding both populations and samples is vitally important when making valid inferences about them based on sample data. When conducting statistical analysis, sample means and standard deviation are used as estimates of population means and standard deviation, respectively; while hypothesis testing aims at testing whether there exists any disparity between sample means and population means.
What is Population in Statistics?
Statistics defines population as any group or set of entities being researched, be they people, animals, plants or any other entities meeting certain criteria that researchers seek to explore. A population may include humans as well as animals or plants if that meets research objectives; its definition can differ depending on your research question and size may also change accordingly. Statistical analysis typically uses samples taken from populations as the foundation to make inferences from them and obtain valid study results; it’s therefore vitally important that researchers gain an in-depth knowledge about them so their sampling efforts will produce valid study results that make statistical analysis results that result in valid conclusions.
Types of Population in statistics
In statistics, populations can be classified into different types based on various characteristics.
Here are some of the types of populations in statistics:
Finite Population
Finite populations refer to populations whose numbers can be identified and counted. An example would be employees in an organization or students at school.
Infinite Population
An infinite population refers to populations wherein its members or objects cannot be counted – for instance, bacteria in a river or stars in the sky.
Homogeneous Population
A homogenous population refers to those where all individuals or objects share similar features, such as all being green apples or male students.
Heterogeneous Population
A heterogeneous population refers to groups in which individuals or objects possess differing traits or features; for instance, fruits from different kinds or students of diverse genders, ages, and ethnic backgrounds could form such a population.
Target Population
Target populations refer to specific groups or objects which the researcher wants to study, such as all residents in one city or all patients diagnosed with specific medical conditions.
What is Sample in Statistics?
Statistics scholars often refer to samples as subsets of populations selected for the study. When studying all individuals or objects of an entire population is impractical or impossible, researchers may utilize samples instead as an effective means for gathering information without studying every person individually or object in every circumstance. When selecting samples, it should reflect these parameters accurately so as not to produce inaccurate and misleading study results. Sampling methods include random, stratified stratification, cluster sampling convenience sampling as methods. Statistical analyses use sample data inferences about population parameters gathered through samples used statistical analyses on samples themselves.
Types of Sample in Statistics
In statistics, several types of samples can be used to collect data from a population.
Here are some of the most common types of samples:
Probability Sampling
Probability sampling is an approach to data collection where each member of a population has an equal and known chance of being selected as part of a sample group, making the selection more random than driven by bias or personal judgment on behalf of researchers.
- Simple Random Sample: In simple random sampling, individuals from a population are selected randomly so that everyone has an equal chance of selection; this method is most frequently employed when dealing with larger populations.
- A stratified sample divides a population into distinct subgroups or strata based on specific attributes such as income, gender, and age; then, individuals from each stratum are randomly selected to form a sample representative of its constituent parts.
- Cluster Sampling: Cluster sampling involves subdividing a population into groups or clusters based on characteristics such as location. After selecting randomly from those clusters, their members form part of the sample set.
- Systematic Sample: In a systematic sample, population members are randomly selected from an overall population at fixed intervals. For instance, every 10th individual from this larger pool might be selected to make up this sampling plan.
- Convenience Sample: In essence, convenience sampling involves selecting individuals from a population based on their willingness or availability to participate. This strategy is usually employed when assembling an accurate representational sample is impossible or impracticable.
Non Probability Sampling
Non-probability sampling is a sampling approach in which participants are selected without using random chance alone; rather, researchers choose their participants based on criteria unrelated to probability, such as availability or willingness.
Some popular methods of non-probability sampling include:
- Convenience Sampling: Participants are selected based on their easy accessibility or proximity to the researcher.
- Quota Sampling: Participants are selected based on predetermined quotas, such as a number of people from specific age or gender groups.
- Purposive Sampling: Participants are selected based on specific criteria, such as expertise or special attributes they bring to a study.
- Snowball Sampling: Participants are selected based on referrals from existing participants.
What is the difference Between Population & Sample?
| Population | Sample |
| A population refers to the entire group of individuals, objects, or events that share common characteristics and are of interest to the researcher | Sample is a subset of the population that is selected using a sampling method |
| The population is typically larger than the sample, as it includes all individuals, objects, or events that meet the researcher’s criteria. | The sample is a smaller group selected from the population. |
| The population is the entire group of interest and is considered to be representative of itself. | A sample may or may not be representative of the population from which it is drawn |
How to Choose a sample from a given population?
Choosing a sample from a given population is often preferred over studying the entire population for several reasons, including:
- Time and resources: It is often not practical to collect data on an entire population due to constraints on time, money, or other resources. Sampling can be a more efficient way to collect data while still obtaining meaningful results.
- Feasibility: In some cases, studying some of the population may only be possible, such as when it is too large or widely dispersed.
- Practicality: Sampling can be a more practical approach when the research question involves destructive testing or when collecting data on the entire population is not feasible due to ethical or legal concerns.
- Representative: When a well-designed sample is selected, it can provide a representative representation of the population, providing an accurate estimate of population parameters, such as the mean or proportion.
- Precision: Sampling can lead to more precise estimates of population parameters by allowing for statistical sample analysis, which can be extrapolated to make inferences about the population.
Conclusion
Understanding the difference between population and sample is integral for statistical analysis. Population refers to all individuals, objects or events we want to investigate, while sample refers to a smaller subset that we select for further examination. Studies that employ samples as part of their methodology often find it impossible or impractical to survey an entire population directly; nevertheless, it remains essential that any selected sample represents it accurately for valid conclusions about that population to be drawn from its data. Sampling techniques and sample size determination are essential in assuring samples’ representativeness. Therefore an in-depth knowledge of the both is imperative in order to avoid bias and ensure validity and reliability in statistical analyses.
