Many people mistakenly juxtapose knowledge for wisdom, as both are nouns and used in the English language. But the truth is that they are different. There are many quotes and sayings of knowledge and wisdom given by the experts and great personalities that defines the significance of these two terms. Sir Fancis Bacon said “Knowledge is power” while Gautam  Buddha, considered “Wisdom is highest of all five virtues”. Knowledge is the accumulation of information, learned through education or experience. On the other hand wisdom is when you how to apply your knowledge, for the benefit of others.

Content: Knowledge Vs Wisdom

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of Knowledge

The term knowledge, simply refers to the understanding or awareness, in relation to a person, thing or subject, such as facts, skills, information, etc. It is the state of what you know about a particular topic. It is the familiarity with different objects, ways of doing things, places, cultures, events, facts, ideas, etc. It can be theoretical or practical proficiency gained over time through education or experience by learning, observing, researching, discussing, studying and so on.

Definition of Wisdom

Wisdom is a wider term than knowledge or intelligence. It is the quality of person to think, act or discern what is right, true and enduring. It is the application of common sense, knowledge and experience, at the right time, place, manner and situation so as to follow the best possible course of action. It develops the ability to judge and make beneficial and productive choices in life.
Knowledge and understanding are the basis of wisdom, but experience is the key to attain wisdom. It unites knowledge and experience into insights and increases an individual’s understanding about relationships and meaning of life.

Conclusion

After referring to the above points, it can be concluded that there is a difference between knowledge and wisdom, but they are connected concepts. Knowledge without wisdom is possible, but wisdom without knowledge is impossible.

While knowledge is limited, wisdom has no visible end. Knowledge can have positive or negative effects in the sense that it can be useful to people if they use in a right way, but can also harm others if they use that knowledge in a wrong direction. Unlike wisdom which has only positive results, because it is the implementation of knowledge with a positive and benevolent attitude.

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Census and sampling are two methods of collecting survey data about population, that are used by many countries. Census refers to the quantitative research method, in which all the members of the population are enumerated. On the other hand, the sampling is the widely used method, in statistical testing, wherein a dataset is selected from the large population, that represents the entire  group. These two survey methods are often contrasted with each other and so this article makes an attempt to clear the differences between census and sampling, in detail; Have a look.

Content: Census Vs Sampling

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of Census

A well organized procedure of gathering, recording and analyzing information regarding the members of the population is called a census. It is an official and complete count of the universe, where in each and every unit of the universe is included in the  collection of data. Here universe implies any region (city or country), a group of people, through which the data can be acquired.

Under this technique, the enumeration is conducted about the population by considering the entire population. Hence this method requires huge finance, time and labor for gathering information. This method is useful, to find out the ratio of male to female, the ratio of literate to illiterate people, the ratio of people living in urban areas to the people in rural areas.

Definition of Sampling

We define sampling as the fraction of the population, so selected in order to represent the characteristics of the larger group. This method is used for statistical testing, where it is not possible to consider all members or observations, as the population size is very large.

As statistical inferences are based on the sampling observations, the selection of the appropriate representative sample is of utmost importance. So, the sample selected should indicate the entire universe and not exhibit a particular section. On the basis of the data collected from the representative samples, conclusion are drawn upon the whole population. For instance: A company places an order for raw material by simply checking out the sample.

The units which constitute sample is considered as ‘Sampling Units’. The full-fledged list containing all sampling units is called ‘Sampling Frame’.

Conclusion

Many people interpret census as the opposite of sampling, in which all the members of the population are taken into account instead of only a fraction. But the census is based on the sampling frame to enumerate population. Therefore, it is quite clear that these two quantitative research methodologies are different, but it can’t be said that one is above the other.

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Many people have mistakenly contrasted survey with the questionnaire. Both of them are methods used in the gathering primary data, whether it is for the marketing of a product, or collecting information from people on social issues. Surveys are the conventional way of carrying out research, whereas questionnaires are a tool of acquiring data on a particular topic. This article is presented for you to know the differences between survey and questionnaire.

Content: Survey Vs Questionnaire

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of Survey

By the term survey, we mean a research process, used for orderly collection and analysis of information, from a group of people to measure opinions, thoughts, experiences, etc. It is not confined to gathering information using questions, but it also encompasses observations, measurement, evaluation of data and judgment of the researcher.

A survey can have different forms like a survey of the whole population is known as the census, but it can also be conducted on a representative sample of a group with a view to draw conclusions on a larger population. Sample survey is a widely used method because of its cost effectiveness, speed, and practical approach. There are many modes of carrying out surveys:

• Face to face survey (Interview)
• Questionnaire
• Telephonic survey
• Postal or mail out survey
• Internet-based survey
  • Email survey
  • Web-based survey

Definition of Questionnaire

The term questionnaire refers to a form, which contains a set of survey questions, so designed, with a view to extracting certain information from the respondent. The instrument includes questions, instructions, and spaces for answers. The questions to be asked are so framed, to obtain straightforward information from the respondents.

A questionnaire has a written and printed format, delivered or distributed to people to provide responses to facts or opinions. The surveyor uses these responses for statistical analysis. It is mainly used for gathering factual information, with an intention to bifurcate people and their circumstances.

Conclusion

‘Survey’ is an umbrella term that includes questionnaire, interview, observation method as a tool for collecting information. Although, the best, quickest and inexpensive way of conducting a survey, is the questionnaire. Surveys are usually conducted for research or studies, while questionnaire is used just to collect information such as job application or patient history form, etc.

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Sampling means selecting a particular group or sample to represent the entire population. Sampling methods are majorly divided into two categories probability sampling and non-probability sampling. In the first case, each member has a fixed, known opportunity to belong to the sample, whereas in the second case, there is no specific probability of an individual to be a part of the sample. For a layman, these two concepts are same, but in reality, they are different in the sense that in probability sampling every member of the population gets a fair chance of selection which is not in the case with non-probability sampling. Other important differences between probability and non-probability sampling are compiled in the article below.

Content: Probability Vs Non-Probability

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of Probability Sampling

In statistics, probability sampling refers to the sampling method in which all the members of the population has a pre-specified and an equal chance to be a part of the sample. This technique is based on the randomization principle, wherein the procedure is so designed, which guarantees that each and every individual of the population has an equal selection opportunity. This helps to reduce the possibility of bias.

Statistical inferences can be made by the researchers using this technique, i.e. the result obtained can be generalized from the surveyed sample to the target population. The methods of probability sampling, are provided below:

• Simple Random Sampling
• Stratified Sampling
• Cluster Sampling
• Systematic Sampling

Definition of Non-Probability Sampling

When in a sampling method, all the individuals of the universe are not given an equal opportunity of becoming a part of the sample, the method is said to be Non-probability sampling. Under this technique as such, there is no probability attached to the unit of the population and the selection relies on the subjective judgment of the researcher. Therefore, the conclusions drawn by the sampler cannot be inferred from the sample to the whole population. The methods of non-probability sampling are listed below:

• Convenience Sampling
• Quota Sampling
• Judgment or Purposive Sampling
• Snowball Sampling

Conclusion

While probability sampling is based on the principle of randomization where every entity gets a fair chance to be a part of the sample, non-probability sampling relies on the assumption that the characteristics are evenly distributed within the population, which make the sampler believe that any sample so selected would represent the whole population and the results drawn would be accurate.

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In our earlier article, we have discussed probability and non-probability sampling, in which we came across types of probability sampling, i.e. Stratified Sampling and Cluster Sampling that are most commonly juxtaposed by the people. There is a big difference between stratified and cluster sampling, which in the first sampling technique, the sample is created out of the random selection of elements from all the strata while in the second method, the all the units of the randomly selected clusters form a sample. Similarly, other significant distinguishing points are discussed in the given article. Have a look.

Content: Stratified Sampling Vs Cluster Sampling

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of Stratified Sampling

Stratified sampling is a type of probability sampling, in which first of all the population is bifurcated into various mutually exclusive, homogeneous subgroups (strata), after that, a subject is selected randomly from each group (stratum), which are then combined to form a single sample. A stratum is nothing but a homogeneous subset of the population, and when all the stratum are taken together, it is known as strata.

The common factors in which the population is separated are age, gender, income, race, religion, etc. An important point to remember is that strata should be collectively exhaustive, so that no individual is left out and also non-overlapping because, overlapping stratum may result in the increase in the selection chances of some population elements. The sub-types of stratified sampling are:

• Proportionate Stratified Sampling
• Disproportionate Stratified Sampling

Definition of Cluster Sampling

Cluster sampling is defined as a sampling technique in which the population is divided into already existing groupings (clusters), and then a sample of the cluster is selected randomly from the population. The term cluster refers to a natural, but heterogeneous, intact grouping of the members of the population. The most common variables used in the clustering population are the geographical area, buildings, school, etc. Heterogeneity of the cluster is an important feature of an ideal cluster sample design. Sub-types of cluster sampling:

• Single-stage cluster sampling
• Two-stage cluster sampling
• Multistage cluster sampling

Conclusion

To end up the discussion, we can say that a preferable situation for stratified sampling is when the identicalness within an individual stratum and the strata mean to vary from each other. On the other hand, the standard situation for cluster sampling is when the diversity within clusters and the cluster should not vary from each other.

Further, sampling errors can be reduced in stratified sampling if between-group differences among strata are increased, whereas the between-group differences among clusters should be minimized to reduce sampling errors in cluster sampling.

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Research is the most widely used tool to increase and brush-up the stock of knowledge about something and someone. In the field of marketing, business, sociology, psychology, science & technology, economics, etc. there are two standard ways of conducting research, i.e. qualitative research or quantitative research. While the former relies on verbal narrative like spoken or written data, the latter uses logical or statistical observations to draw conclusions. There is a never-ending debate on which research is better than the other, so in this article, we are going to shed light on the difference between qualitative and quantitative research. Have a look.

Content: Qualitative Research Vs Quantitative Research

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of Qualitative Research

Qualitative research is one which provides an understanding of human and social science, that studies highly complex phenomena that are impossible to elucidate with the quantitative research. Although, it generates ideas or hypothesis for later quantitative research.

Qualitative research is used to gain an in-depth understanding of human behavior, experience, attitudes, intentions, and motivations, on the basis of observation and interpretation, to find out the way people think and feel. It is a form of research in which the researcher gives more weight to the views of the participants. Case study, grounded theory, ethnography, historical and phenomenology are the types of qualitative research.

Definition of Quantitative Research

Quantitative research is a form of research that relies on the methods of natural sciences, which produces numerical data and hard facts. It aims at establishing cause and effect relationship between two variables by using mathematical, computational and statistical methods. The research is also known as empirical research as it can be accurately and precisely measured.

The data collected by the researcher can be divided into categories or put into rank, or it can be measured in terms of units of measurement. Graphs and tables of raw data can be constructed with the help quantitative research, making it easier for the researcher to analyze the results.

Conclusion

An ideal research is one, which is conducted by considering both the methods, together. Although, there are some particular areas which require, only one type of research which mainly depends on the information required by the researcher.  While qualitative research tends to be interpretative, quantitative research is concrete.

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Before elucidating the difference between IQ and EQ, let us first understand, what the terms actually mean. EQ or say Emotional Quotient refers to a person’s ability to understand his/her emotion along with the other person’s emotions, whereas IQ stands for Intelligence Quotient indicates a person’s intelligence level. Many people hold that IQ is more important for a person to achieve success in life while researchers hold that people with high EQ are more successful in their careers. So, it is one of the hot topics for debates, that which is better than the other. Check out the article below to find some interesting distinguishing points between these two.

Content: IQ Vs EQ

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of IQ

Intelligence Quotient, shortly known as IQ is an intelligent test score, obtained by a standardized intelligence test in which an individual’s mental age is divided by his chronological age and then multiplied by 100. The test aims at assessing a person’s cognitive capacity of thinking and reasoning. The term was first introduced by William Stern.

IQ is a measure of the intelligence level of an individual, that is reflected in the score obtained by the person in the intelligence test as compared to the scores received by other people of your age on the same test.

Definition of EQ

EQ is an abbreviation for the term Emotional Quotient, which is defined as a person’s capability of identifying his own and other’s person’s emotions. It is a measure of emotional intelligence level of an individual, which demarcates between different feelings and use this intelligence to guide thinking and behavior.

The term was first evolved in 1995, by Daniel Goleman a psychologist in his book on Emotional Intelligence. It is the ability of a person to identify, express and control his her thoughts and actions, understand other people and rightly interpret other their situations, make right and quick decisions, cope with pressures and crisis and so on. It has been researched that people with high EQ have better mental health, job performance, way of living life.

Conclusion

The IQ and EQ level is different in different persons. It is possible that a person has a high IQ level, but low EQ count and vice versa. Therefore, the methods of checking IQ and EQ also differs.

Your IQ is what  determines your competencies and individual capacities, but your EQ determines how you interact and treat people in your life, it shows how you are going to cope with pressures or face crisis. The difference between these two is that no one can alter his/her intelligence or reasoning abilities, but anyone can learn to handle emotions, to bring success for him.

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Data collection plays a very crucial role in the statistical analysis. In research, there are different methods used to gather information, all of which fall into two categories, i.e. primary data, and secondary data. As the name suggests, primary data is one which is collected for the first time by the researcher while secondary data is the data already collected or produced by others. There are many differences between primary and secondary data, which are discussed in this article. But the most important difference is that primary data is factual and original whereas secondary data is just the analysis and interpretation of the primary data.

Content: Primary Data Vs Secondary Data

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of Primary Data

Primary data is data originated for the first time by the researcher through direct efforts and experience, specifically for the purpose of addressing his research problem. Also known as the first hand or raw data. Primary data collection is quite expensive, as the research is conducted by the organization or agency itself, which requires resources like investment and manpower. The data collection is under direct control and supervision of the investigator.

The data can be collected through various methods like surveys, observations, physical testing, mailed questionnaires, questionnaire filled and sent by enumerators, personal interviews, telephonic interviews, focus groups, case studies, etc.

Definition of Secondary Data

Secondary data implies second-hand information which is already collected and recorded by any person other than the user for a purpose, not relating to the current research problem. It is the readily available form of data collected from various sources like censuses, government publications, internal records of the organization, reports, books, journal articles, websites and so on.

Secondary data offer several advantages as it is easily available, saves time and cost of the researcher. But there are some disadvantages associated with this, as the data is gathered for the purposes other than the problem in mind, so the usefulness of the data may be limited in a number of ways like relevance and accuracy. Moreover, the objective and method adopted for acquiring data may not be suitable to the current situation. Therefore, before using secondary data, these factors should be kept in mind.

Conclusion

As can be seen from the above discussion that primary data is an original and unique data, which is directly collected by the researcher from a source according to his requirements. As opposed to secondary data which is easily accessible but are not pure as they have undergone through many statistical treatments.

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In financial jargon, the terms investment and speculation are overlapping and used synonymously. In investment, the time horizon is relatively longer, generally spanning at least one year while in speculation, the term may extend up to half year only. As per Benjamin Graham, an American economist, and professional investor, investment is an activity, which upon complete analysis assures the safety of the amount invested and adequate return. Conversely, speculation is an activity which does not satisfy these requirements. So this article makes an attempt to clear the differences between investment and speculation. Have a look.

Content: Investment Vs Speculation

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of Investment

Investment refers to the acquisition of the asset, in the expectation of generating income. In a wider sense, it refers to the sacrifice of present money or other resources for the benefits that will arise in future.

Nowadays, there is a range of investment options available in the market as you can deposit money in the bank account, or you can acquire property, or purchase shares of the company, or invest your money in government bonds or contribute in the funds like EPF or PPF. There are two main elements of investments i.e. time and risk.

Investments are majorly divided into two categories i.e. fixed income investment and variable income investment. In fixed income investment there is a pre-specified rate of return like bonds, preference shares, provident fund and fixed deposits while in variable income investment, the return is not fixed like equity shares or property.

Definition of Speculation

Speculation is a trading activity that involves engaging in a risky financial transaction, in expectation of making enormous profits, from fluctuations in the market value of financial assets. In speculation, there is a high risk of losing maximum or all initial outlay, but it is offset by the probability of significant profit. Although, the risk is taken by speculators is properly analyzed and calculated.

Speculation ca be seen in markets where the high fluctuations in the price of securities such as the market for stocks, bonds, derivatives, currency, commodity futures, etc.

Conclusion

At the end of this discussion, it is can be said that both are different and should not be used interchangeably. Investors play a very crucial role in maintaining liquidity in the market but speculators too, play a major character in absorbing excessive risk and providing required liquidity, at the time when investors do not participate.

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Inflation refers to the rate at which the overall prices of goods and services rises resulting in the decrease in the purchasing power of the common man, which can be measured through Consumer Price Index. Modern analysis of inflation revealed that it is mainly caused either by demand side or supply side or both the factors. Demand side factors result in demand-pull inflation while supply side factors lead to cost-push inflation, which are the two types of inflation. This article explains clearly the significant difference between Demand-pull and cost-push inflation.

Content: Demand-Pull Inflation Vs Cost-Push Inflation

1. Comparison Chart
2. Definition
3. Key Differences
4. Conclusion

Comparison Chart

Definition of Demand-Pull Inflation

Demand Pull Inflation arises when the aggregate demand goes up rapidly than the aggregate supply in an economy. In simple terms, it is a type of inflation which occurs when aggregate demand for products and services outruns aggregate supply due to monetary factors and/or real factors.

Demand-Pull Inflation due to monetary factors: One of the major cause of inflation is increase in money supply than the increase in the level of output. The German inflation, in the year 1922-23 is the example of Demand-Pull Inflation caused by monetary expansion.

Demand-Pull Inflation due to real factors: When the inflation is due to any one or more of the following reasons, it is said to be caused by real factors:

• Increase in government spending without change in tax revenue.
• Fall in tax rates, with no change in government spending
• Increase in investments
• Decrease in savings
• Increase in exports
• Decrease in imports

Out of these six factors, the first four factors, will result in the rise in the level of disposable income. Increase in aggregate income result in increase in aggregate demand for goods and services, causing demand pull inflation.

Definition of Cost-Push Inflation

Cost push inflation means the increase in the general price level caused by the rise in prices of the factors of production, due to shortage of inputs i.e. labor, raw material, capital etc. It results in decrease in the supply of outputs which mainly use these inputs. So, the rise in prices of the goods is emerged from the supply side.

Moreover, cost-push inflation may also be caused by depletion of natural resources, monopoly, natural disaster and so on. There are three kinds of cost-push inflation:

• Wage-push inflation: When the monopolistic groups of the society like labor union exercise their monopoly power, to enhance their money wages above the competitive level, which cause an increase in the cost of production.
• Profit-push inflation: When the monopoly power is used by the fimrs operating in monopolistic and oligopolistic market to increase their profit margin, leading to rise in the price of goods and services.
• Supply shock inflation: A type of inflation arising due to unexpected fall in the supply of necessary consumer goods or major industrial inputs.

Conclusion

Therefore, you can conclude with the above discussion the main reason for causing inflation in the economy is either by demand-pull or cost-push factors. It is often argued that which is the supreme factor for inflation, which one of the two-factor causes rise in the general price level for the first time. Experts hold that demand-pull factor the leading factor for inflation in any economy.

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