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]]>Measuring a small portion of something and then making a general statement about the whole thing is known as sampling.
Since it is generally impossible to study an entire population (e.g. many individuals in a country, all college students, every geographic area etc.) researchers typically rely on sampling to acquire a section of the population to perform an experiment or observational study.
It is important that the group selected be representative of the population and not biased in a systematic manner.
For example, a group comprised of wealthiest individuals population in a given area probably would not accurately reflect the opinion of the entire population in that area.
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A method of sampling that uses random selection so that all units/cases in the population have an equal probability of being chosen is known as probability or random sampling.
Advantages:
1. Easy to conduct.
2. High probability of achieving a representative sample.
3. Meets assumptions ofmany statistical procedure.
Disadvantages:
1. Identification of all members of the population can be difficult.
2. Contacting all members of the sample can be difficult.
Sampling that does not involve random selection and methods are not based on the10rationale of probability theory is nonprobability or nonrandom sampling.
Simple random sampling is a probability sampling method.
Definition : A Simple Random Sample (SRS) consists of n individuals from the population chosen such a way that every individual has the equal chance to be selected.
Advantage:
1. Simple to conduct.
2. Each unit has an equal chance of being selected.
Disadvantage :
1. Complete list of individuals in the universe is required.
There is a gap or interval between each between each selected species of the sample.
Selection of units is based on sample interval k starting from a determined point where k= N/n.
Steps:
1. Number the units on your frame from 1 to N, and the population are arranged in the same way.
2. First sample drawn between 1 and K randomly. (Determine present/ random start.)
3. Afterwards, every k th must be drawn, untill the total sample has been drawn.
A stratified random sample is obtained by dividing the Population elements into nonoverlapping groups, called strata and then selecting a random sample directly and independently from each stratum.
A stratified SRS is a special case of stratified sampling that uses SRS for selecting units from each stratum.
Cluster Sampling is defined as a sampling technique in which the elements of the population is divided in existing groups (cluster).
Then a sample from the cluster is selected randomly from the population.
subtypes:
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Objectives of this articleTo understand

Read the previous article: Introduction to biostatistics part 1
In statistics, a population is a set of homogeneous items or events that is of interest for some question or experiment.
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Example: Birth weights of all babies in a particular hospital in month May, the monthly expenditure of nonresidential students of 2nd year in Department of Botany, etc.
A sample is a smaller group of members of a population selected as a representative of the population.
In statistical inference, a subset or a portion of the population (a statistical sample) is chosen to represent the population in statistical analysis. If a sample is chosen properly, characteristics of the complete population that the sample is drawn from can be estimated from corresponding characteristics of the sample.
Selecting samples being unbiased is called random sampling. A very important feature of a good study is that the sample is randomly selected from the target population. Randomly means that every member of the target population has an identical chance of being included in the sample. In other words, the method you use for selecting your sample can not be biased.
It’s important because if you select your subjects in a biased way, then your results will also be biased. And more likely, it wouldn’t represent the population.
Read more about Random Sampling
Suppose you’re making a phone survey on the job satisfaction of Bangladeshis. If you call them at home during the day between 8 a.m. and 5 p.m. you’ll miss out on all those who work or has a job during the day; it could be that day workers are more satisfied than night workers.
Or your boss told you to make a survey of the overall development of the office from the year 2010 to 2020. To make your boss happy while sampling you chose the things as samples that actually developed but didn’t count those which almost remained the same or demoted in 10 years.
Like the abovementioned examples, there are many ways of being biased. To get a good survey it’s necessary to avoid all kinds of biasness possible.
References
Mahajan BK 2002. (Methods in Biostatistics)(6th edition)
Deborah Rumsey (Statistics for Dummies)
You want to calculate the percentage of female versus male shoppers at a department store. So on a Saturday morning, you place data collectors at each of the store’s four entrances for three hours, and you have them record how many men and women enter the store during that time.
1. Why collecting data at the store on one Saturday morning for three hours can cause bias in the data?
Ans: E. All of these choices are true.
Bias is systematic favoritism in the data. You want to get data that represents all customers at the store, irrespective of what day or what time they shop, whether they shop in couples or alone, and so on. You can’t assume that the people who shopped during those three hours on that Saturday morning are representative of the store’s total clients. This sample wasn’t drawn randomly — everyone who walked in was counted.
Parameter is a summary value that describes the population such as its mean, variance, correlation coefficient, proportion, etc.
Statistic is a summary value that describes the sample as its mean, standard deviation, standard error, correlation coefficient, proportion, etc.
The value is calculated from the sample and is often applied to the population but may or may not be a valid estimate of the population.
There is a good reason that the population parameter and sample static will vary; hopefully very little but can be significant too if the sample is biased or too small (i.e. doesn’t represent the population properly).
Q&A
You’re assigned to a job where you have to know what percent of all households in a large city has a single woman as the head of the household. To calculate this percentage, you survey 200 households and determine how many of these 200 are headed by a single woman.
1. In this example, what is the population?
2. In this example, what is the sample?
3. In this example, what is the parameter?
4. In this example, what is the statistic?
1. Ans: All households in the city.
A population is a complete or whole group you’re interested in studying. The aim here is to estimate what percent of all households in a large city has a single woman as the head of the household. The population is total households and the variable is whether a single woman runs the household.
2. Ans: Selected 200 households.
The sample is a portion or subset drawn from the entire population you’re interested in studying. In this inference, the sample is the 200 households selected out of all the households in the city.
3. Ans: The percent of households headed by single women in the city.
A parameter is some characteristic of the population. Because studying a population directly isn’t generally possible, parameters are usually estimated by using statistics (numbers calculated from sample data).
4. Ans: The percent of households headed by single women within the 200 selected households.
A statistic is a number representing some characteristic that you calculate from your sample data; the statistic is used to estimate the parameter (the same characteristic in the population).
Reference
Statistics: 1,001 Practice Problems For Dummies
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