The primary difference between parameter vs statistic is that a parameter is a numerical figure that describes an entire population set (for example, the population mean). In contrast, a statistic is a numerical figure that denotes a sample (e.g., sample mean).
Quantitative research seeks to understand population characteristics by identifying metrics. However, in actuality, collecting data from every individual in the population is sometimes too complex, timeconsuming, or impossible. So instead, information is gathered from samples.
The sample statistics can be utilized to develop informed estimations about population parameters using inferential statistics.
The difference between population and sample:
To understand the difference between parameter vs statistic, we first need to look at the difference between population and sample.
A population is a complete set of subjects or groups that you’re interested in examining in research. It can be a group of individuals (for example, allfemale mature individuals in the United Kingdom or workers in a specific sector). Still, it can also be a collection of items,organizationss, events, nations, species, animals, etc.
A sample is a subset of the entire population. It is a set of items from which data can be gathered.
Example: Population vs sample
You are looking to know how many people in the United States favour the death penalty. Unfortunately, it is not practicable to gather data from the entire population since the demographic you’re focused on is all American nationals. So instead, you survey a group of 2000 people using random sampling.
Parameters and statistics: What type of numbers they are.
Statistics and parameters are numerical representations of any quantifiable attribute of a population or sample.
The most frequent parameter vs statistic for categorical variables (e.g., religious affiliation) is a proportion.
The standard or mean deviations serve as statistics or parameters for numeric variables (e.g., weight).
Sample Statistic 
Population parameter 
The percentage of 2000 people who support the death penalty, as determined by a random sampling. 
The percentage of people in the United States who support the death sentence. 
The average earnings of 800 students from the Boston and Wellesley colleges. 
The average earnings of all students attending college in Massachusetts. 
The standard deviation of weight of mangoes grown on a single farm. 
The standard deviation of the weights of all mangoes in the area. 
Average screen time of 1500 Indian primary school pupils. 
Average screen time of all Indian students attending primary school. 
Statistical notation
The next step to understanding the difference between parameter vs statistic is understanding what a statistical notion is.
To indicate whether it is a sample that is being referred to or a population, distinct symbols are used for parameters vs statistics.
Greek and capital letters usually denote populations. On the other hand, Latin and lowercase letters denote samples.
Symbols used to denote the difference between statistics vs parameters
Sample Statistic 
Population parameter 

Proportion 
p̂ 
P 
Standard deviation 
s 
σ 
Mean 
x̄ 
μ 
Variance 
s2 
σ2 
Differentiating between a parameter vs statistic
It can be difficult to ascertain whether a number is a statistic or a parameter in news and research publications. Ask yourself the following questions to determine which sort of number you’re working with:
If the response to both queries is yes, the number is most certainly a parameter. Thus, data from the entire population can be collected and summarised in parameters for small populations.
If neither of these questions is answered positively, the figure is probably a statistic. Sampling is a technique for gathering data from large groups and applying statistical generalizations to a larger population that is externally valid.
Using statistics to estimate parameters
The next step in understanding the difference between parameter vs statistic is how statistics can be used to estimate parameters.
You may estimate population parameters from sample statistics employing inferential statistics. However, your sample should preferably represent your population and be randomly picked to provide unbiased estimates.
There are different kinds of population parameter estimations that you may make. These include point estimates and interval estimates.
Both forms of estimations are critical for determining the likely source of a parameter.
Example of estimating population parameter using a sample statistic
You discover that 61 per cent of participants in your sample favour the death sentence among US residents. You construct a point estimate and an interval estimate from your sample statistic to estimate the population parameter.
In this case of parameter vs statistic, the point estimate is the sample statistic: you estimate that 61 per cent of all Americans favour the death sentence.
You create a 95 per cent confidence interval to determine the interval estimate, which informs you where the population parameter is likely to lay mostly. So, for example, when using random sampling, there’s a 0.95 chance that the real population parameter for death penalty support among the citizen of the US is somewhere in the range of 57 and 65 per cent.
Samples help to make deductions regarding population. In addition, because samples are practical, costeffective, straightforward, and easy to control, they offer a much simpler approach to collect data from.
When a study requires data from every member, populations are used. Data can be collected from the population when it is limited and is easy to access.
The properties of a data collection are summarized using descriptive statistics. In addition, you may test a hypothesis using inferential statistics or see if your data is broadly applicable to a larger population.
A statistic is a measure of the sample, whereas a parameter measures the entire population.
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