When it comes to using surveys or analyzing qualitative/quantitative data sources it’s essential that you understand what each type of data means. Specifically, parameter vs statistic because it can impact your understanding of the data.
In this guide, you’ll quickly learn the difference so you’re in a better position to interpret data from third party sources, present your own data, or simply understand what you’re looking at.
What is a statistic?
A statistic is a quantity that’s computed from a sample of a larger population. A statistic is used when it’s impossible to accurately understand the disposition of an entire population.
Note: population, when considering parameter vs statistic, means anything that’s representative of a group. It could be rocks, horses, humans, cars, etc.
For example, when opinion polls are carried out, the conclusion may be that 55% of people like ice cream or are for stricter environmental protections.
It’s nearly impossible to survey an entire nation so researchers interact with a representative sample. That may be a few hundred or a few thousand people depending on the total estimated population size.
The result is a statistic that represents the entire population. It’s important to note that statistics can be heavily skewed if the representative sample isn’t chosen correctly.
For example, if you do research on average earnings in a population but only go to lower-income areas, the statistic will be flawed. The information is strictly true but it won’t give you real insights.
What is a parameter?
A parameter is a quantity that’s representative of an entire population. It’s used less often than a statistic because it’s inherently difficult to take stock of large populations over a few thousand.
For example, if you took a survey of a 250-person company you may discover that 80% of people approved of the CEO.
It’s relatively simple to compel everyone in the organization to take the survey. Whatever conclusions you draw can be said to be 100% representative of the population.
Hence, you would call it a parameter.
Though it doesn’t suffer from the same downsides as a statistic due to sampling errors, it has other weaknesses. If you’re using surveys to gather data, there could be confirmation bias or acquiescence bias.
It’s important to structure your surveys in a way that will reduce the number of errors it’ll generate. That means paying attention to how you ask questions and the types of questions you ask.
Parameter vs statistic
To recap, a statistic is a representative sample of a larger grouping (population) that’s difficult or impossible to directly measure. A parameter is a quantity that’s representative of an entire group or population.
- A parameter is an entire population or group
- A statistic is a representative sample of the entire group
- A statistic is a portion of a population that has a fixed but unknown size and is directly proportional to the size of the entire population
- A parameter is only applicable to smaller populations
Examples of statistics
If you’re presented with a fact about a very large population like the age breakdown of trees in a forest or the opinions of an entire country – it’s likely a statistic. Here are a few examples to better illustrate the point:
- 30% of the adults in the United States have gone to at least one professional basketball game. It’s not feasible to survey hundreds of millions of people to get a data set like this so it’s clearly a statistic.
- 21% of European Union residents feel like having one currency made their economies more robust. Like the first example, the EU has hundreds of millions of residents and it would require massive capital and time to survey all of them.
- 7% of adult men over 21 have never had sex. There are roughly 3 billion adult men in the world. No organization would be able to survey all of them to produce a parameter so this is a statistic.
- 45% of the people in Atlanta have some form of tertiary education. Atlanta has a population of around 500,000 people so it would be difficult to survey all of them. This is a statistic.
When in doubt, think about the time and cost that would be associated with getting the data for a parameter. Would it require millions of dollars and thousands of people? If so, it’s likely not a parameter and instead, it’s a statistic.
Examples of parameters
If you’re presented with a fact about a relatively small population like the voting habits of House of Representatives members or employees in a company – it’s more likely to be a parameter. Here are a few examples of parameters.
- 98% of state legislatures have male and female members. This is a parameter because there are only 50 states in the Union. The population is small enough that a sample isn’t needed.
- 10% of the students in Jackson Heights Elementary failed the standardized tests this year. Since the exact number of students in the school is known and accounted for, the percentage is representative of the entire population.
- 45% of the animals in the LA Zoo are older than 5 years. There are detailed records of every animal in the zoo and the population is relatively small. This is likely a parameter.
10% of the students in Jackson Heights Elementary failed the standardized tests this year. Since the exact number of students in the school is known and accounted for, the percentage is representative of the entire population.
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When considering whether a figure is a parameter, consider the size of the population and if it’s small enough to be counted directly. Or think about whether or not the information is a fact about the whole population. If yes, then it’s likely a parameter.
Final word on Parameter vs statistic
When you think about parameter vs statistic, don’t focus on whether one is more important than the other. Instead, consider which one is more suitable for the situation at hand.
For instance, in the academic realm, students navigating the complexities of distinguishing between parameters and statistics often seek statistics assignment help to deepen their understandingand accurately apply these concepts in their scholarly endeavors.
Additionally, engaging with specialized guidance facilitates a more intuitive grasp of when to apply parameters and statistics, enriching students’ analytical skills. This direct mentorship aids in navigating the subtleties of statistical analysis, bridging the gap between theoretical knowledge and practical application, thereby preparing students for more complex data interpretation tasks in their future academic and professional pursuits.
We often have limited data from which to draw conclusions and make decisions. Instead of getting hung up on what it should be called, make sure the data you have is reliable.
Whether or not it’s a statistic or a parameter, consider the techniques used to gather the data and if it’s reliable. If so, you can make the right decision with confidence.
