Analytical skill

‘Deductive reasoning is a basic form of valid reasoning, commencing with a general statement or hypothesis, then examines the possibilities to reach a specific, logical conclusion’.[10] This scientific method utilises deductions, to test hypotheses and theories, to predict if possible observations were correct.[11]

A logical deductive reasoning sequence can be executed by establishing: an assumption, followed by another assumption and finally, conducting an inference. For example, ‘All men are mortal. Harold is a man. Therefore, Harold is mortal.’[11]

For deductive reasoning to be upheld, the hypothesis must be correct, therefore, reinforcing the notion that the conclusion is logical and true. It is possible for deductive reasoning conclusions to be inaccurate or incorrect entirely, but the reasoning and premise is logical. For example, ‘All bald men are grandfathers. Harold is bald. Therefore, Harold is a grandfather.’ is a valid and logical conclusion but it is not true as the original assumption is incorrect.[12] Deductive reasoning is an analytical skill used in many professions such as management, as the management team delegates tasks for day-to-day business operations.[13][14]

Inductive reasoning compiles information and data to establish a general assumption that is suitable to the situation.[15] Inductive reasoning commences with an assumption based on faithful data, leading to a generalised conclusion. For example, ‘All the swans I have seen are white. (Premise) Therefore all swans are white. (Conclusion)’.[15] It is clear that the conclusion is incorrect, therefore, it is a weak argument. To strengthen the conclusion, it is made more probable, for example, ‘All the swans I have seen are white. (Premise) Therefore most swans are probably white (Conclusion)’.[15] Inductive reasoning is an analytical skill common in many professions such as the corporate environment, where statistics and data are constantly analysed.[16]

Doctor using abductive reasoning to diagnose a man with diabetes

1. Generalised: This manner utilises a premise on a sample set to extract a conclusion about a population.[15]
2. Statistical: This is a method that utilises statistics based on a large and viable random sample set that is quantifiable to strengthen conclusions and observations.[15]
3. Bayesian: This form adapts statistical reasoning to account for additional or new data.[15]
4. Analogical: This is a method that records on the foundations of shared properties between two groups, leading to a conclusion that they are also likely to share further properties.[15]
5. Predictive: This form of reasoning extrapolates a conclusion about the future based on a current or past sample.[15]
6. Causal inference: This method of reasoning is formed around a causal link between the premise and the conclusion.[15]

Abductive reasoning commences with layered hypotheses, which may be insufficient with evidence, leading to a conclusion that is most likely explanatory for the problem.[17] It is a form of reasoning where the conductor chooses a hypothesis that would best suit the given data.[18] For example, when a patient is ill, the doctor gathers a hypothesis from the patient's symptoms, or other evidence, that they deem factual and appropriate. The doctor will then go through a list of possible illnesses and will attempt to assign the appropriate illness. Abductive reasoning is characterised by its lack of completeness, in evidence, explanation or both.[18] This form of reasoning can be creative, intuitive and revolutionary due to its instinctive design.[18]

Critical thinking can be developed through establishing personal beliefs and values.[23] It is critical that individuals are able to query authoritative bodies: teachers, specialists, textbooks, books, newspapers, television etc.[23] Querying these authorities allow critical thinking ability to be developed as the individual gains their own freedom and wisdom to think about reality and contemporary society, revering from autonomy.[23]

Critical thinking can be developed through probability models, where individuals adhere to a logical, conceptual understanding of mathematics and emphasise investigation, problem-solving, mathematical literacy and the use of mathematical discourse.[23] The student actively constructs their knowledge and understanding, while teaching models function as a mediator by actively testing the student through querying, challenging and assigning investigation tasks, ultimately, allowing the student to think in deeper ways about various concepts, ideas and mathematical contexts.[23]

Verbal communication is interaction through words in linguistic form.[27] Verbal communication consists of oral communication, written communication and sign language. It is an effective form of communication as the individuals sending and receiving the information are physically present, allowing immediate responses. In this form of communication, the sender uses words, spoken or written, to express the message to the individuals receiving the information.[28]

Verbal communication is an essential analytical skill as it allows for the development of positive relationships among individuals.[29] This positive relationship is attributed to the notion that verbal communication between individuals fosters a depth of understanding, empathy and versatility among them, providing each other with more attention.[29] Verbal communication is a skill that is commonly used in professions such as the health sector, where healthcare workers are desired to possess strong interpersonal skills.[30] Verbal communication has been linked to patient satisfaction.[31] An effective strategy to improve verbal communication ability is through debating as is it fosters communication and critical thinking.[32]

Non-verbal communication is commonly known as unspoken dialogue between individuals.[33] It is a significant analytical skill as it allows individuals to distinguish true feelings, opinions and behaviours, as individuals are more likely to believe nonverbal cues as opposed to verbal expressions.[33] Non-verbal communication is able to transcend communicational barriers such as race, ethnicity and sexual orientation.[33] This is a significant factor for international industries that require global communication or communication between other regions such as trade between China and The United States of America.[34]

Dancing is a common expressionist form of human non-verbal communication.

Statistical measures showcase that the true meaning behind all messages is 93% non-verbal and 7% verbal.[33] Non-verbal communication is a critical analytical skill as it allows individuals to delve deeper into the meaning of messages. It allows individuals to analyse another person's perceptions, expressions and social beliefs.[35] Individuals who excel in communicating and understanding non-verbal communication are able to analyse the interconnectedness of mutualism, social beliefs and expectations.[35]

A communication theory is an abstract understanding of how information is transferred from individuals.[36] Many communication theories have been developed to foster and build upon the ongoing dynamic nature of how people communicate. Early models of communication were simple, such as Aristotle's model of communication, consisting of a speaker communicating a speech to an audience, leading to an effect.[37] This is a basic form of communication that addresses communication as a linear concept where information is not being relayed back.

Modern theories for communication include Schramm's model where there are multiple individuals, each individual is encoding, interpreting and decoding the message, and messages are being transferred between one another.[38] Schramm has included another factor in his model in experience i.e. expressing that each individual's experience influences their ability to interpret a message.[38] Communication theories are constantly being developed to acclimatise to certain organisations or individuals. It is imperative for an individual to adopt a suitable communication theory for organisations to ensure that the organisation is able to function as desired.[39] For example, traditional corporate hierarchy are commonly known to adopt a linear communicational model i.e. Aristotle's model of communication.[40]

In higher education, new research provides the most desired quality of evidence, if this is not available, then existing forms of evidence should be used.[41] It is accepted that research provides the greatest form of knowledge, in the form of quantitative or qualitative data.

Research students are highly desired by various industries due to their dynamic mental capacity. Research students are commonly sought after due to their analysis and problem-solving ability, interpersonal and leadership skills, project management and organisation, research and information management and written and oral communication.[44]

Text analysis is the discovery and understanding of valuable information in unstructured or large data.[48] It is a method to transform raw data into business information, allowing for strategic business decisions by offering a method to extract and examine data, derive patterns and finally interpret the data.[49]

Statistical analysis involves the collection, analyses and presentation of data to decipher trends and patterns.[50] It is common in research, industry and government to enhance the scientific aspects of the decision that needs to be made. It consists of descriptive analysis and inferential analysis.[51]

Descriptive analysis provides information about a sample set that reflects the population by summarising relevant aspects of the dataset i.e. uncovering patterns.[52] It displays the measures of central tendency and measures of spread, such as mean, deviation, proportion, frequency etc.[53]

Inferential analysis analyses a sample from complete data to compare the difference between treatment groups.[54] Multiple conclusions are constructed by selecting different samples. Inferential analysis can provide evidence that, with a certain percentage of confidence, there is a relationship between two variables. It is adopted that the sample will be different to the population, thus, we further accept a degree of uncertainty.[55]

Example of sales forecasting, a form of predictive analysis

Diagnostic analysis showcases the origin of the problem by finding the cause from the insight found in statistical analysis.[56] This form of analysis is useful to identify behavioural patterns of data.[57]

Predictive analysis is an advanced form of analytics that forecasts future activity, behaviour, trends and patterns from new and historical data.[58] Its accuracy is based on how much faithful data is present and the degree of inference that can be exploited from it.[59]

Prescriptive analytics provide firms with optimal recommendations to solve complex decisions.[60] It is used in many industries, such as aviation to optimise schedule selection for airline crew.[60]