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Differences between Parameter and Statistic with Comparison Chart

24 Dec 2018 12:43 pm || 0

Although statistics and parameter appear to be similar terms that may be used interchangeably, there lies a sea of difference between the two. The clue to this discrepancy between parameters vs statistics lies with the fact that while a parameter considers any and every person involved in an entire population, statistics would include the data it receives from a selected sample while ignoring the presence of the rest of the community. If this still sounds confusing, read on to understand how this slight technicality can affect crucial results.

Statistic vs Parameter

Here is a list of the significant difference between parameter and statistic.

Statistics Parameter
It yields the actual result with regards to specific characteristics. It yields the most probable estimate as a result concerning specific characteristics.
Not convenient to use for a large population, especially if you cannot locate all the units. Convenient to use for a large population, even if you cannot locate all the units.
The result derived from parameter is fixed. The result derived from statistics is liable to vary with the size of the population.
Statistics require more time to conduct the survey. Parameter requires less time to conduct the survey.
This method increases the cost of the survey. Less cost involved in conducting the survey.
It is less resorted to for a survey. It is more resorted to for of survey.

What is Parameter?

A parameter describes the characteristics of an entire population. The attributes may be the mean, median or mode data derived from the elements taken as a whole. Here, the term population includes every unit that shares a familiar character relevant to the characteristics of the study.

However, this limits the utility of a parameter to the study of a small population, in which every single unit can be located with certainty. For large people where this is not possible, the parameter fails.

Parameter Example:

A parameter example is the quantity of calcium included in the daily diet of all middle school children of a single school. Here, every middle school child is accounted for, and the data can be obtained without missing a single unit included in the population.

Another parameter example can be the number of cases of tuberculosis reported in a specified group of hospitals over a specified time. In this case, too, every unit of the population is accounted for without miss.

What is a Statistic?

Unlike parameter, statistics only take into consideration a sample of the total population. This may be a random sampling or a result of some predefined factor for choosing the sample. Although in statistics, not every unit of the community needs to be taken into account, the size of the sample should be large enough to ensure the accuracy of the data thus obtained.

Despite less accuracy when compared to the parameter, using statistics is the more convenient option when you have to collect data from a large set of population whose every unit you cannot precisely account for. To ensure better accuracy in statistics, you can rely on previous experience and analytical tools such as variance and standard deviation.

Statistics Example:

A statistics example is the number of people who use to feel it is better to use the public bus compared to the local train for going to the office. Since it is not practical or feasible to ask every single person for an opinion, the opinion of a sample is taken into account. The rest is derived from the patterns exhibited in the data.

Another statistics example may be the number of people who prefer evening walk to a morning walk. Again, taking every single person into account will result in a vast volume of data that will be chaos to work with. It is better to record the opinion of a sample of the population instead.

Conclusion

Therefore, from the evaluation of parameters vs statistics, it is amply clear that both entail different purposes and gives different results. In data analysis, parameters vs statistics remain to be among the infamous topics of discussion. Therefore, one must be careful with the use of the two terms as it can potentially cause miscommunication, besides resulting in gross miscalculation.