Basic Terminologies(Parameters, Statistic, Sample, Population, Descriptive and Inferential)

Statman Monday, August 30, 2021

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Outline:

Terminologies:

In Statistics, like any other topic, has its own lingo. The language is what enables you to understand what an issue requires, what results are needed, and how to describe and assess the outcomes in a statistically correct manner. Here's a rundown of several statistical terminologies:

In statistics, researchers, and educators commonly use the terms population and sample (Alferez & Duro, 2006).

Population and Sample

Population

The population refers to the total collection of all elements/experimental units that are being considered or investigated in a statistical investigation.

Sample

A sample is a subset of the population. The sample must be representative of the full population in some way if it is to provide information about the entire population. In reality, a sample cannot be expected to be representative of a population unless it is chosen at random. This is because any nonrandom sampling rule will almost always result in a sample that favors some data values over others.

  • For example, if we want to determine what the average household income in Zamboanga del Norte is, the population of interest is the collection of all Zamboanga del Norte households. However, due to certain limits, such as funding, time, and manpower, we would have to redefine the interest. This time, we used sampling size to limit the scope of the study to only cover the collection of all houses in Dapitan and Dipolog City, as this is done by sampling procedures.

    • Example: Population

  • All registered voters in Zamboanga del Norte.

    • Example: Sample

  • 280 registered voters in Zamboanga del Norte.

    • Parameter and Statistic

    Parameter

    Parameters are numerical values that describe a population's characteristic and are usually denoted by Greek letters such as population standard deviation σ and population mean μ.

    Statistic

    Statistic is a numerical measurement describing some characteristics of a sample. The symbols x and s are statistics which is unbiased of the parameter μ and σ.

    Example: Parameter

  • Proportion of all Filipino residents that against the death penalty.
  • Standard deviation of weights of all Jackfruits in the region IX.

    • Example: Statistic

  • Median income of 350 college students in Jose Rizal Memorial State University.
  • Mean income of the 100 subscribers to a particular magazine.

    • Two Major Areas of Statistics

    When analyzing data, such as the grades received by 100 students for a piece of coursework, descriptive and inferential statistics can be used to analyze their grades. In most studies involving groups of people, descriptive and inferential statistics will be used to analyze the data and develop conclusions. So, what is the difference between descriptive and inferential statistics?

    Descriptive Statistics

    Is concerned with the methods for collecting, organizing, and describing a set of data so as to yield meaningful information (Walpole, 2000). Construction of tables, charts, and graphs and the computation of descriptive statistical measures also fall in this area.

    Descriptive statistics is the name given to a type of data analysis that helps to describe, show, or summarize data in a comprehensible way so that patterns might emerge. Descriptive statistics, on the other hand, do not allow us to draw conclusions beyond the data we've examined or to form conclusions about any hypotheses we've proposed. They're just a way of describing our info.

    Example: Descriptive

    For example, if we had the results of 100 pieces of students' coursework, we may be interested in the overall performance of those students. We would also be interested in the distribution or spread of the marks. Descriptive statistics allow us to do this. Measures of central tendency and Measures of spread are often used to describe data


    Inferential Statistics

    Inferential Statistics is also called Inductive Statistics or Statistical Inference. Comprises those procedures for drawing inferences or making generalizations about characteristics of a population-based on partial and incomplete information obtained sample data to infer to populations.

    Example: Inferential

    For example, let's say we're interested in all of University X students' exam results. However, it is not possible to assess all of students' exam results. As a result, we will now examine the grades of a smaller group of students, such as 1000 pupils. This sample will now represent the significant number of University X students in the population. This sample would be considered for our statistical research of the population from which it was derived.


    "I'm sure I don't have all of the information about the basic terminologies right here. "What other statistical terms would you like to share with us?" I would love to hear your thoughts in the comments section."

    References:

  • https://www.cliffsnotes.com/study-guides/statistics/sampling/populations-samples-parameters-and-statistics
  • https://www.scribbr.com/statistics/parameter-vs-statistic/
  • https://corporatefinanceinstitute.com/resources/knowledge/other/parameter/
  • https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php
  • https://study.com/academy/lesson/descriptive-and-inferential-statistics.html#:~:text=Descriptive%20statistics%20uses%20the%20data,from%20the%20population%20in%20question.
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    1 Comments

    1. StatmanSeptember 12, 2021 at 6:41 PM

      Thank you for reading! Any thoughts about basic terminologies of statistcs. I'd love to hear your comment.

      ReplyDelete