Practical Implications In Statistics – 3 Areas Practical Implications Apply In Statistics

Practical Implications In Statistics – 3 Areas Practical Implications Apply In Statistics

What is a Practical Implication?

In its most basic form, a practical implication can be regarded as the precise decisions or activities which can be adopted and implemented as a result of a research study or finding. In other words, it provides basic ways to apply a research finding or conclusion in an actual situation or incidence.

Due to their importance, practical implications are used in diverse settings and situations. In an educational or academic environment, for instance, practical implications are actualized via study exams, field visits, research publications, etc.

This implies that it significantly impacts the entire academic curriculum and evaluation of the students in an institution. Excluding its vitality in an academic setting, it is also applicable and useful in diverse environments, such as business settings, hospitals, financial institutions, etc.

Nonetheless, for the sake of this article, practical implications and their impacts or usefulness will chiefly be examined through statistical lenses.

What are Statistics?

As a field of study, the key component of statistics is the collection and examination of data. To put it in another way, it is an area of study that focuses on gathering, organizing, analyzing, interpreting, and presenting data. These definitions, although straightforward, are complex in the most peculiar way.

This is so because many subjects, objects, and items fall under the data category. Thus, data in this context can broadly be described as a compendium or body of information, statements, and calculations which can be utilized to infer decisions or draw conclusions.

Placing the fore mentioned definition of data into a statistical context or setting, the concept of data covers all the categories of subjects, objects, and items that identify as data.

What are the examples of data in a statistical setting or context?

Numerous forms of data can be analyzed, gathered, and evaluated in a statistical context or setting. Nonetheless, these factors largely depend on the research goal or the nature of the subject matter being studied or examined. These diverse forms of data include;

Quantitative or Numerical Data:

Often, numerical data is identified or affiliated with quantitative data. In other words, most of the data in this form or category involves numbers and their values (i.e., how much, how many, or how often). Similarly, data, in this context, can be grouped into two major categories or classes, namely, discrete data and continuous data.

Discrete data comprises a limited number of likely values that can be chosen from within a given range and can be numbered or specific. For instance, the number of people in an office at a specific period can be regarded as discrete data. Continuous data, on the other hand, can take any value between two numbers, no matter how small, and is measured on an endless scale.

This suggests that, regardless of the type of size of the scale, continuous data can measure any numerical value. For instance, a person’s weight can be regarded as continuous data.

Categorical Data:

Categorical data consists of categorical variables which may be quickly grouped or categorized. In other words, it describes data or variables which can easily be grouped or categorized. These categories or classifications may either be nominal or ordinal.

Nominal data, in this context, can be described as a subset of categorical data that depicts traits or attributes which can be identified or labelled but lack specific or determined order. Examples of nominal data include; eye colour (brown, blue) and religion (i.e., Muslim, Christian, etc).

As mentioned in the proposed notion, ordinal data lacks specific order, and as a result of such, it can not be efficiently rated or assessed on a numerical scale. Still, it can be simplified or converted into frequencies, percentages, etc., further examined with tools such as contingency tables.

On the other hand, Ordinal data is another subset of categorical data depicting attributes or features that may be placed in specific order or rank. However, the values in these ranks need not necessarily be important or equal to one another. Examples of ordinal data include; educational degrees (i.e. bachelor’s, master’s, PhD, etc.).

Unlike nominal data, ordinal data can be placed in specific orders or ranks, and as such, it can be represented on a number line. Similarly, descriptive statistics, such as frequencies, percentages, medians, etc., can be used to evaluate ordinal data.

Other minor forms of data, excluding those that are not included in the types mentioned earlier of statistical data and their subsets, include;

• Time Series Data contains data imputed and gathered at regular intervals or periods. Examples of such include; stock prices, population statistics, etc.
• Cross-Sectional Data contains data collected across a specific period or point in time. Examples of such includes; survey results, census results, etc.

• Panel Data consists of several observations made by the same individuals or entities over a specified time. In other words, it can be described as a synthesis of cross-sectional data which includes diverse observations by people or things at a particular period in time.

  Examples of panel data include; survey data collected by individuals for a prolonged time, sales data for a locality or vicinity, collected by sellers and buyers in a particular time, etc.

How do practical implications apply in statistics?

In its most credulous state, practical implications in statistics can be depicted as the modes or methods in which statistical methods and findings can be applied or implemented to real-life or practical instances and situations.

As properly identified and distinguished in the preceding sections of this article, statistics largely borders on data collection and evaluation. Data is present in virtually all human activities in this context and the general sense. For instance, mere observation of people’s religion or eating habits within a particular region or locality can be regarded or identified as data collection or evaluation.

This implies that statistics is present in all forms of human activities and, as such, it can be applied or implemented practically after much study and evaluation.

Thus, the capacity of statistics to measure risks and douse out all forms of uncertainty is a major mode in which it can be applied and implemented practically. For instance, intricate or in-depth study in a particular manner can give a person the ability to predict the events or activities associated with that particular subject matter and calculate the risks associated with such study.

A simpler example or illustration will be studying the data and happenings in a foreign exchange (FOREX) market. A regular trader in this market studies the happening by collecting and evaluating statistical data over a particular period.

This data collected is then used as a yardstick to guide the trader’s future and practical trading decisions. This can be used to avoid the undue loss the trader may have stumbled upon if he/she failed to conduct the proposed research or study.

Similarly, statistics may be employed practically to ascertain the efficacy of actions or interventions. In other words, it can be used to ascertain whether or not an action or intention is carrying out the responsibility or action it was created to do. In such a scenario, the desired result and the current results being exhibited as a result of the proposed action will be compared side by side.

For example, an entrepreneur who curated an innovative idea to help grow or develop a particular aspect of a business can compare both results by providing statistical percentages of the desired action and the proposed action. This could amount to the creation of better innovative ideas which could be used to aid or ensure the desired results.

These provided instances posit that statistical results, data and models can be used to ensure effective decision-making in the practical sense by providing predictions and forecasts based on the study of historical data and to evaluate or examine the efficacy of a decision that is already in motion.

What areas are practical implications in statistics deemed as the most relevant?

Practical implications in statistics are deemed to be useful in virtually all aspects of human activities. However, it is commonly patronized in the following fields;

• In educational settings, teachers use statistical data to evaluate students’ behaviour, draft school curricula, and provide assignments or research papers.
• In business settings whereby entrepreneurs use statistical data to predict their future monetary activities, curate innovative ideas and avoid unnecessary risks.
• In finance institutions, statistical data is collected to create predictions and forecasts for investors and shareholders to avoid risks and ensure commendable profits.