Your upcoming statistics research project has fried your brains. You’re confused between parameter vs statistic and which to choose for your assignment. Pretty sure you’re two vague blogs away from having a breakdown.
With all the brain fog, you might think both parameters and statistics are the same. That’s not true! But before we delve into details, you must calm down and stick with us until the end.
Assuming statistic and parameter are same is a common mistake students make. Yet, this smoke is not without any fire. Both of these terms are often used synonymously in layperson’s terms.
But conducting research with such assumptions is a recipe for disaster. So why take a few steps back and start with the basics? Let’s begin with the standard definitions and then move forward.
Within statistical analysis, a parameter is a numerical value that describes the entire population. Can’t recall what the population is? We’ve got you covered. A population is the entire group of subjects we’re trying to explore.
These subjects include animals, objects, phenomena (like transactions), and people. Greek letters, denote A true population parameter such as:
Examples for Parameter:
Suppose we find an average percentage in a class of 30 students. We can study each student’s grade to deduce an average.
Similarly, we want to estimate the median income of all employees working in one company. Let’s say the company has around 2000 employees.
Its not practical to count or measure each individual’s salary. Yet we can take a representative sample of employees and calculate the average salary.
The parameter, in this case, would be the average salary of all employees working in the company.
To explore parameter vs. statistic, let’s look at statistic examples. Contrary to parameters, we have statistics. A statistic is a characteristic of a sample that estimates population parameters.
A sample is an extraction or a subset of the entire population. A sample size represents the population. Statistic too, are often denoted by Roman letters such as;
Example For Statistic
To highlight the difference of parameter vs statistic need proper examples. So, let’s continue with the previous example.
We take a representative sample of 100 employees working in that company to calculate the average salary. The calculated value is a statistic. It can estimate the population parameter, which is the average salary of all employees working in the company.
If this information was helpful, continue reading for some key differences. But students, if you’re too anxious about reading a lengthy blog. And need help finding something that resonates with your question. We suggest you reach our professional for statistics assignment writing help service.
So by now, you must have a clear idea of the subject size difference between parameter and statistic. A Parameter is often unknown and estimated from a population. On the contrary, a statistic is calculated directly from the sample. In most research, the size of parameters is not defined.
To better understand this point, let’s look at these examples:
We want to know the average height of a particular family. Now by this statement alone, we can estimate two things:
All the members of this family would take part in the study. (So, it’s a parameter)
We only know how many members there are in the family once we conduct the research.
What if we’re trying to find the average height of 6th graders? We’d take a random sample statistic. Estimated between 80-100 sixth graders to determine the average height. Here we’d have an actual size of the subject before conducting the study.
Ideally, a researcher will use statistics if they are researching a larger group. They’ll pick a random number of subjects from that larger group as a sample. However, researching smaller groups involves the use of parameters.
Many of you are wondering which one of these can provide accurate answers for your research. But easy there, you eager beaver, it’s complicated! We know that the researchers are observing the entire subject group within parameters.
This ensures accuracy, and the answers are spot on. Alternatively, when we talk about samples, they might not represent the whole population to the tee. Thus, we can’t claim that the results we got from random sampling methods are 100% accurate. (Or applicable to the entire population)
Okay, brace yourself for some disappointing but pretty self-explanatory facts. Although parameters derive error-free results, most authentic life researchers use statistics. Imagine you’d have to survey everyone in your school or college.
Now that’s just a massive waste of your time and efforts. That’s why the statistic approach is in most real-life research around us, including:
Let’s get one thing out of the way, most biases in research exist because of the researchers. Biased selection of a sample for statistical approach misrepresents the whole population.
It is universally acknowledged that comparison is only complete with similarities. So, here are some of the similarities between parameters and statistics.
Unlike the difference between both approaches, the similarities are pretty straightforward.
Excellent, you’re still around! Now that we got the comparison out the face let’s talk about some pros and cons. Now, we will explore certain advantages and disadvantages of these study approaches separately.
By now, we expect you, Sherlocks, to understand the essential parameters to research. Here are five advantages of using parameters as a data analysis approach in research
Parameters provide better standards for measuring a phenomenon than any other deduction approach. Many researchers use existing results to deduce new results. Or even recreate the pre-existing. This makes parameters more reliable and valid for future research.
Numbers are a symbol of accurate or calculated results. Assigning numeric values to any subject helps the researcher deduce accurate results.
Not just that, it also helps them keep track of overtime changes in their parameters. This assists in identifying trends and patterns more easily.
Parameters can help to improve the generalizability of research findings. By defining parameters, researchers can identify the conditions under which their findings hold. Extending their findings to other contexts or populations can prove very useful.
This can be particularly important in psychology or sociology. Such research findings are often used to inform policy decisions or interventions.
More often than not, researchers replicate parameters. This is particularly important in fields such as science and medicine. Results in such fields need verification from other researchers. Without which no new treatment or therapy can develop.
For example:
Results from “Psychological Issues in Teens” are applicable to related researches. Like “Juvenile cases studies” and their on-set parameters. The pre-existing results would help the new research in deducing more accurate results.
Like any other numerical approach, parameters provide a common language. Though for researchers to describe their findings. This facilitates communication and collaboration among researchers from different fields or backgrounds.
By using parameters, researchers can communicate complex information more clearly and concisely.
Previous information might’ve led you to think parameters are the best research approach. Yet, that’s not the case. There are some downsides to using parameters in research. Here are five potential disadvantages of Parameters.
Parameters are usually context-specific, which can limit the generalizability of research findings. Findings based on specific parameters may not apply to other contexts or populations.
This generally reduces the impact of the research. If you’re applying findings from one context, ensure they resonate with your research.
Assigning numerical values to parameters requires measurement, which is subject to error. Measurement error can occur due to imperfect instruments, human error, or other factors. This can lead to inaccurate results and a lack of confidence in the findings.
Defining parameters can limit the scope of research. Its difficult to adapt to unexpected findings or changes in the research environment.
Researchers may need to modify parameters as they learn more about any phenomena. This can be challenging if the parameters have already been defined.
Defining parameters can sometimes oversimplify the complexity of the phenomena under research. It reduces a complex phenomenon to a set of numerical values. Researchers may lose important information about the context and nuances of the phenomenon.
Parameters are often based on assumptions about the phenomena under study. If these assumptions are accurate, the resulting parameters may also be accurate. This can lead to faulty conclusions and misguided recommendations.
Phew! It’s not only you who are glad that’s over. Now let’s move on to the other key component of the research.
A statistical approach to collect data analysis is the one key component of research methodology. Let’s explore some of the advantages of using statistics.
The statistical approach applies to larger bounds of data. It is a powerful tool to analyze and identify concealed aspects like;
Using statistical analysis; researchers can draw meaningful conclusions from large, complex datasets.
Statistics allows researchers to generalize their findings to larger populations. Statistical methods can estimate population parameters from a sample.
These can then make inferences about the entire population. This is particularly important in fields such as public health. Because these results can help in developing new policies and interventions
Statistics allow researchers to quantify the reliability of their findings. Statistical methods can calculate the probability of obtaining a particular result by chance.
These can provides a measure of the reproducibility of the findings. This is crucial for ensuring that research findings are reliable and valid.
Inferential statistics allow researchers to quantify the reliability of their findings. This can help create a more object opinion on different phenomenon.
From Political problems to customer service, statistical approaches can deduce public opinion. Compared to parameters, statistic is more practical, efficient, and less time consuming.
Statistical analysis can raise ethical concerns. Mainly when it involves using sensitive data or the potential for harm. Researchers must ensure safe and appropriate use of statistical analysis. The results of their research needs honest reporting.
Unfortunately, misuse of statistics is common for personal and political gain. This leads to biased or inaccurate reporting of research findings.
Researchers must be aware of the potential misuse. They need to ensure that their findings are not misrepresented or misinterpreted.
Misinterpretation and misuse of statistical analysis can be complex. Researchers may draw conclusions not supported by the data. Or based their results on incomplete or biased analysis.
Statistical analysis must meet certain assumptions. These include normality of data distribution or independence of observations. Violations of these assumptions can lead to inaccurate results.
Moreover, statistical models can only sometimes capture the complexity of the phenomenon. This can limit their usefulness to some degree.
More reliance on probability can lead to accurate results. These are not a direct measure of effect size or practical significance. Though sample data size and other factors can effect them.
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A parameter is a number that represents characteristics of an entire population. For example; What is the average weight of a husky dog? This is a parameter because it enquires something about the entire population of huskies.
A population or sample can have different aspects that a researcher can study. Having a wider range of attributes for evaluation requires different types of parameters and statistics. Two common types are continuous and categorical.
Continuous scale usually studies the mean, correlations, standard deviations and medians.
Categorical scale is used to explore proportions or poles
The inferential statistics allows hypothesis testing and assess if your data represents the larger population. Descriptive Statistics sum up characteristics of data set.
You need to inspect if:
If the answer to both questions is yes, then its a parameter. If not, then its a statistic.
To make deductions about population we need samples. They make collecting data very cost-effective, efficient, and practical.
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