A population contains all members of a specified group Example: The population may be "ALL people living in the Turkey"
A sample data set contains a part, or a subset, of a population. The size of a sample is always less than the size of the population from which it is taken. Example: The sample may be "SOME people living in the Turkey"

Nominal scale is a scale of measurement that is used for identification purposes. A nominal scale describes a variable with categories that do not have a natural order or ranking.
Ordinal Scale involves the ranking or ordering of the attributes depending on the variable being scaled.
Interval scale of data measurement is a scale in which the levels are ordered and each numerically equal distances on the scale have equal interval difference, but no absolute zero.
Ratio scale has all the properties of an interval variable, and also has a clear definition of zero. When the variable equals zero, there is none of that variable.

Unlike ordinal variables that take values with no standardized scale, every point in the interval scale is equidistant. Arithmetic operations can also be performed on the numerical values of the interval variable.
The interval scale of data measurement is a scale in which the levels are ordered and each numerically equal distances on the scale have equal interval difference.

Observational study - data are observed and collected on each subject and no manipulation of the subjects’ environment occurs.
Experimental study- Manipulate the subjects’ environment, then measure the response variable.

Dealing with unstructured and structured data, Data Science is a field that comprises everything that related to data cleansing, preparation, and analysis. Data science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning the data. In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data.

Statisticians are the most sought-after professionals these days.We are living in the information age, and the Statistician is the king of handling, analysing, interpreting and facilitating decisions from data. The principal reason why Statistics is a key necessity to humankind today is the massive amounts of available information.

The main criteria for selecting a sample will be that the sample is representative of the population and that there’s no or very little subjectivity in the choice of the sampling units.

The easiest form of data is called categorical, or qualitative. Categorical variables and data can be either nominal or ordinal. Exam grade is an ordinal categorical variable, since its categories are ordered: A is better than a B, B is better than a C, and so on. Other examples of nominal categorical variables are gender, region of residence, field of study, type of transport, type of housing, etc.

Categorical variables and data can be either nominal or ordinal. Examples of nominal categorical variables are gender, region of residence, field of study, type of transport, type of housing, etc. There is no ordering in the categories of these variables. . Exam grade is an ordinal categorical variable, since its categories are ordered: A is better than a B, B is better than a C, and so on. Other examples of ordinal categorical variables are income group (If incomes have been categorized), an attitude question in a survey where possible responses are strongly agree/agree/disagree/strongly disagree (these categories have an order).

The other main type of data is called continuous, or quantitative, for example data on variables “blood pressure”, “age” and “income”. These are observations of variables on continuous scales, usually rounded in some convenient way. For example, although age is a continuous time variable, and we are getting older all the time by seconds, minutes and hours, someone’s age is almost always rounded to the number of years completed. There is a subtle difference between interval-scale and ratio-scale continuous data, which is worth mentioning here. Age is an interval-scale variable: to compare two children of ages 10 and 12, we would compute the interval difference, .e. 2 years.

As a general rule, most data on monetary values and those coming from physical measurements (e.g., lira, gold price, centimeters, and kilograms) are ratio-scale variables.

What are the “big data” sets today and where do they come from? These are mostly found in the following areas:

• Commerce, especially online electronic commerce
• Finance, for example share prices on stock markets, all managed electronically
• Insurance, all the premiums, incidents, actuarial transactions in an insurance company
• Biomedicine, especially in genetics, where information is literally exploding as gene-sequencing reveals and codes the total genetic profile of a person
• Transport, for example in the airline industry, all the flights, all the passengers
• Climate data, measurements from tens of thousands of weather stations across the world

As a general rule, most data on monetary values and those coming from physical measurements (e.g., lira, gold price, centimeters, and kilograms) are ratio-scale variables.

The first role of Statistics is to reduce this mass of complex data to a simpler form in order to facilitate understanding and learning from the data. This is often done by making graphical representations of the data. Statistics can make a lot of numerical data easily understandable. The world today abounds in textual data. Words, SMSs, tweets, social media posts, verbal responses in questionnaires, these can all be treated as data. Climate data, measurements from tens of thousands of weather stations across the world, is an example of big data.

The other main type of data is called continuous, or quantitative, for example data on variables “blood pressure”, “age” and “income”. These are observations of variables on continuous scales, usually rounded in some convenient way. For example, although age is a continuous time variable, and we are getting older all the time by seconds, minutes and hours, someone’s age is almost always rounded to the number of years completed. There is a subtle difference between interval-scale and ratio-scale continuous data, which is worth mentioning here. Other measures of time are interval-scale variables, for example hours of sleep (on Sundays I sleep an hour longer - I would not say I sleep 14% longer).

Statistics is at the heart of understanding climate patterns and making weather forecasts. The first role of Statistics is to reduce this mass of complex data to a simpler form in order to facilitate understanding and learning from the data. This is often done by making graphical representations of the data.

It may seem that everything may be recorded and stored somewhere. But in reality - unless we somehow centralize and link all the databases in the world, and have free access to them - we can get access to only a small part of whatever data we are interested in. For example, it is impossible to ask the whole population of Turkey what their view on climate change is, whether they believe it is natural or manmade. This is where the most basic concept in Statistics comes into play: sampling from a population.

Categorical variables and data can be either nominal or ordinal.
The question about climate change, with possible responses “natural”, “manmade” or “don’t know/can’t answer” is a nominal categorical variable, as is the variable “country” - there is no ordering in the categories of these variables. By contrast, exam grade is an ordinal categorical variable, since its categories are ordered: A is better than a B, B is better than a C, and so on.

There is a subtle difference between interval-scale and ratio-scale continuous data, which is worth mentioning here. Age is an interval-scale variable: to compare two children of ages 10 and 12, we would compute the interval difference, i.e. 2 years. We would not say the 12-year old is 20% older than the 10-year old. But comparing prices or incomes, for example, we would tend to compute percentage differences, making them ratio-scale variables. A good example is the inflation rate, comparing the prices of a basket of products over time, not as a difference but as a percentage. As a general rule, most data on monetary values and those coming from physical measurements (e.g., lira, gold price, centimeters, kilograms) are ratio-scale variables.

The world today abounds in textual data. Words, SMSs, tweets, social media posts, verbal responses in questionnaires, these can all be treated as data. Some recoding will be necessary, since text is not numerical. Frequently occurring words can be counted, the lengths of sentences can be measured, the number of words used just once can be identified, and so on, in order to create quantitative variables from text. Textual data have been used, for example, in identifying the author of threatening letters, in comparing political party manifestos, in classifying respondents in a survey who give answers to open ended questions.

Medical research is a good context to understand these differences - Statistics in medical research is often called Biostatistics.

The way the sample is collected is crucial to obtaining a valid estimate, and this is an important subject which will be dealt with in this course. The main criteria for selecting a sample will be that the sample is representative of the population and that there is no or very little subjectivity in the choice of the sampling units. Sampling is not only conducted by survey researchers on human populations, but also by auditors on a company’s accounts, by agricultural researchers on different pieces of land, and by quality control inspectors on products in a factory, to name only a few examples.

In order to be able to prove that aspirin is the cause of the improvement in health, an experiment needs to be conducted where conditions are controlled between those taking aspirin and those not taking it. Such an experiment might be designed as follows, restricted to men, for example, since the effects are suspected to be different for men and women.

In order to be able to prove that aspirin is the cause of the improvement in health, an experiment needs to be conducted where conditions are controlled between those taking aspirin and those not taking it. Such an experiment might be designed as follows, restricted to men, for example, since the effects are suspected to be different for men and women.

At the start of this Introduction we asked “What is Statistics? What is Data Science? What is Analytics?” To deal with Analytics first, this is a term now used in business circles as a substitute for the word Statistics, but it really means the same thing. The word Statistics is considered by some people, especially businessmen, as a bit old-fashioned, and sometimes even difficult to pronounce! But don’t be fooled: Analytics is a fancy word for Statistics.
When it comes to Data Science, however, the term does have some different meaning. Data Science is a field that includes Statistics as well as areas such as Computer Science, Database Management and Data Visualization, for example, and has come into being mainly as a result of the spectacular growth in the amount of available data in this new information world that we live in.

Recommended Correction
Page 3
When 4 is added to 5, the result is exactly 9; when hyrdrogen and nitrogen are synthesized,…
When 4 is added to 5, the result is exactly 9; when hydrogen and nitrogen are synthesized,…
Data come in the form of numbers as well as text, which is something we are discovering more and more. We live in an information world and information is the virtual gold of our society. All observations can be reduced to some numerical quantity, and there are even fields of digital philosophy and digital sociology. (Page 6)
…The key to all of the above is the phrase: “a sample of about 1000 carefully selected people”. The way the sample is collected is crucial to obtaining a valid estimate, and this is an important subject which will be dealt with in this course. The main criteria for selecting a sample will be that the sample is representative of the population and that there is no or very little subjectivity in the choice of the sampling units. Sampling is not only conducted by survey researchers on human populations, but also by auditors on a company’s accounts, by agricultural researchers on different pieces of land, and by quality control inspectors on products in a factory, to name only a few examples. (Page 7)
As also understood from the information given, the correct answer isI, II, III, IV and V.

Data come in various forms and are measured in different ways. For example, a doctor measures your blood pressure with an instrument, a survey researcher asks you a question and you give a verbal response, you go on holiday to a particular country, you are at a certain age and have a certain income, you have a grade for a completed university course, or this past week you have gone to the supermarket a certain number of times, and bought a certain basket of products, etc. Blood pressure, question response, country, age, income, course grade, number of supermarket visits and purchased products, these are all statistical variables.
As also understood from the information given, the correct answer is I, II, III, IV and V.

The easiest form of data is called categorical, or qualitative, for example data on variables “country” (e.g., the data observation might be Germany) or “question response” (e.g., believe that climate change is manmade) or “exam grade” (e.g., B). Categorical variables and data can be either nominal or ordinal. The question about climate change, with possible responses “natural”, “manmade” or “don’t know/can’t answer” is a nominal categorical variable, as is the variable “country” - there is no ordering in the categories of these variables. By contrast, exam grade is an ordinal categorical variable, since its categories are ordered: A is better than a B, B is better than a C, and so on.
Other examples of nominal categorical variables are gender, region of residence, field of study, type of transport, type of housing, etc.
Other examples of ordinal categorical variables are income group (if incomes have been categorized), an attitude question in a survey where possible responses are strongly agree/agree/disagree/strongly disagree (these categories have an order), social class (with classes usually in an inherent order), terrorist threat levels (in the UK these are low/moderate/substantial/severe/critical), etc.
The other main type of data (see Fig. 1.4) is called continuous, or quantitative, for example data on variables “blood pressure”, “age” and “income”. These are observations of variables on continuous scales, usually rounded in some convenient way. For example, although age is a continuous time variable, and we are getting older all the time by seconds, minutes and hours, someone’s age is almost always rounded to the number of years completed. There is a subtle difference between interval-scale and ratio-scale continuous data, which is worth mentioning here. Age is an interval-scale variable: to compare two children of ages 10 and 12, we would compute the interval difference, i.e. 2 years. We would not say the 12-year old is 20% older than the 10-year old. But comparing prices or incomes, for example, we would tend to compute percentage differences, making them ratio-scale variables. A good example is the inflation rate, comparing the prices of a basket of products over time, not as a difference but as a percentage. As a general rule, most data on monetary values and those coming from physical measurements (e.g., lira, gold price, centimeters, kilograms) are ratio-scale variables.
As also understood from the information given, the correct answer is I, II, III and IV.

Data come in various forms and are measured in different ways. For example, a doctor measures your blood pressure with an instrument, a survey researcher asks you a question and you give a verbal response, you go on holiday to a particular country, you are at a certain age and have a certain income, you have a grade for a completed university course, or this past week you have gone to the supermarket a certain number of times, and bought a certain basket of products, etc. Blood pressure, question response, country, age, income, course grade, number of supermarket visits and purchased products, these are all statistical variables. The observations made on these variables constitute the data. The subjects or individuals or companies on which these observations are made are called cases. Thus, cases might be a group of tourists entering Turkey, for each of whom we have data on which country they come from, how many days they plan to stay in Turkey, what their tourism objectives are (e.g., cultural events, beach holiday, etc.). Or cases might be hospital patients, on whom we have measured the standard medical indicators such as blood pressure, cholesterol levels, blood sugar, and so on.
As also understood from the information given a group of tourist entering Turkey is case, so the correct answer is A group of tourists entering Turkey. “which country they come from”, “how many days they plan to stay” and “what their tourism objectives are” are statistical variables.

The easiest form of data is called categorical, or qualitative, for example data on variables “country” (e.g., the data observation might be Germany) or “question response” (e.g., believe that climate change is manmade) or “exam grade” (e.g., B). Categorical variables and data can be either nominal or ordinal. The question about climate change, with possible responses “natural”, “manmade” or “don’t know/can’t answer” is a nominal categorical variable, as is the variable “country” - there is no ordering in the categories of these variables. By contrast, exam grade is an ordinal categorical variable, since its categories are ordered: A is better than a B, B is better than a C, and so on.
Other examples of nominal categorical variables are gender, region of residence, field of study, type of transport, type of housing, etc.
Other examples of ordinal categorical variables are income group (if incomes have been categorized), an attitude question in a survey where possible responses are strongly agree/agree/disagree/strongly disagree (these categories have an order), social class (with classes usually in an inherent order), terrorist threat levels (in the UK these are low/moderate/substantial/severe/critical), etc.
As also understood from the information given, “Region of Residence”, “Field of Study”, “Type of Transport” and “Type of Housing” are nominal categorical variables. Exam grade is ordinal categorical variable.

The other main type of data (see Fig. 1.4) is called continuous, or quantitative, for example data on variables “blood pressure”, “age” and “income”. These are observations of variables on continuous scales, usually rounded in some convenient way. For example, although age is a continuous time variable, and we are getting older all the time by seconds, minutes and hours, someone’s age is almost always rounded to the number of years completed. There is a subtle difference between interval-scale and ratio-scale continuous data, which is worth mentioning here. Age is an interval-scale variable: to compare two children of ages 10 and 12, we would compute the interval difference, i.e. 2 years. We would not say the 12-year old is 20% older than the 10-year old. But comparing prices or incomes, for example, we would tend to compute percentage differences, making them ratio-scale variables. A good example is the inflation rate, comparing the prices of a basket of products over time, not as a difference but as a percentage. As a general rule, most data on monetary values and those coming from physical measurements (e.g., lira, gold price, centimeters, kilograms) are ratio-scale variables. Other measures of time are interval-scale variables (the word “interval” gives you a clue to that!), for example hours of sleep (on Sundays I sleep an hour longer - I would not say I sleep 14% longer) and time to run 100 meters (e.g., at the 2009 World Championships in Berlin, Usain Bolt shaved more than a tenth of a second off his record, clocking 9.58 seconds - we wouldn’t say he reduced the time from 9.69 seconds by 1.1%).
As also understood from the information given “Age”, “Hours of Sleep” and “Time to Run 100 meters” are interval scale variables, so the correct answer is I, II and V. “The Inflation Rate” and “Gold Price” are ratio-scale variables.

The world today abounds in textual data. Words, SMSs, tweets, social media posts, verbal responses in questionnaires, these can all be treated as data. Some recoding will be necessary, since text is not numerical. Frequently occurring words can be counted, the lengths of sentences can be measured, the number of words used just once can be identified, and so on, in order to create quantitative variables from text. Textual data have been used, for example, in identifying the author of threatening letters, in comparing political party manifestos, in classifying respondents in a survey who give answers to open-ended questions.
As also understood from the information given, the correct answer is IV and V. “Verbal responses in questionnaires” and “Social media posts” need recoding in order to create quantitative variables. “Frequently occurring words”, “The lengths of sentences” and “The number of words used just once” are the ones which are recoded in order to create quantitative variables.

A study that involves Statistics will clearly involve a process of data collection. But there are differences in study objectives which are important to recognize. The main distinguishing factor is whether the study aims to find some evidence of a result or whether it aims to find causes of a result. Medical research is a good context to understand these differences - Statistics in medical research is often called Biostatistics.
It is true that many benefits have been detected of taking a low-dosage of aspirin every day. For example, it has been observed that people who take aspirin regularly (less than 300 mg per day) generally have less health problems such as heart attacks, strokes and cancers. The keyword is “observed”: in a community health study involving thousands of people there will be many that take aspirin regularly for problems such as back pain or headaches. For those people it could be observed that they have less chronic diseases compared to people who don’t take aspirin. This is evidence of a difference, but it does not prove that aspirin is the actual cause of the improved health. People who do not take aspirin daily might be poorer and neglect taking medication, and they suffer from more chronic diseases than people with a higher income, so the real difference is socio-economic. From such an observational study it is not possible to conclude a causal effect, it just gives some tentative evidence of a possible relationship between the treatment and the outcome.
In order to be able to prove that aspirin is the cause of the improvement in health, an experiment needs to be conducted where conditions are controlled between those taking aspirin and those not taking it. Such an experiment might be designed as follows, restricted to men, for example, since the effects are suspected to be different for men and women. Suppose we take a large group of men in the age group 60- 70 years of age that have no history of chronic disease. We divide them into two groups so that the groups are “balanced” in terms of known factors such as age, social class, and so on (we don’t want one group to have older men than the other). By the way, this “dividing into two groups” is not a trivial matter, but that is a subject for later in this course. Assuming the two groups are comparable, then one group is given the daily low dose of aspirin and the other not, and the groups are followed up for a five-year period and then compared for the incidence of health problems that develop during this time. If the group taking aspirin has less health problems, this would indicate a beneficial effect caused by the aspirin.
This type of experiment on people has all sorts of problems, but it is really leading to a conclusion about whether aspirin is the real cause of differences between the two groups. There is the ethical problem of not giving the daily aspirin, which has suspected benefits, to a large group of people. There is also the problem that one group knows it is taking the medication and might change their lifestyle to favour a good outcome by living more healthily. This effect can be eliminated by not telling either group what they are getting and giving the non-aspirin group a so-called placebo, which is medication in the form of an aspirin, so both groups think they are getting the medication.
As also understood from the information given, “A study which aims to find some evidence of a result” is an example of observational studies, so the correct answer is only I. “A study which aims to find causes of a result”, “A study in which the groups are “balanced” in terms of known factors” and “A study conducted where conditions are controlled” are the examples of experimental studies.

To deal with Analytics first, this is a term now used in business circles as a substitute for the word Statistics, but it really means the same thing. The word Statistics is considered by some people, especially businessmen, as a bit old-fashioned, and sometimes even difficult to pronounce! But don’t be fooled: Analytics is a fancy word for Statistics. When it comes to Data Science, however, the term does have some different meaning. Data Science is a field that includes Statistics as well as areas such as Computer Science, Database Management and Data Visualization, for example, and has come into being mainly as a result of the spectacular growth in the amount of available data in this new information world that we live in. The need has been recognized for someone who not only has statistical skills, but also advanced programming skills and knowledge about handling huge data sets, the so-called “Big Data” of today. Thus a new profession has been born, that of the Data Scientist. Again, don’t be fooled: the main skill of a data scientist is knowledge of Statistics,
As also understood from the information given, the correct answer is I and V. “Statistics” and “Analytics” really mean the same thing. Statistics, Computer Science, Database Management and Data Visualization are the areas which Data Science includes. They do not mean the same thing.

What are the “big data” sets today and where do they come from? These are mostly found in the following areas:

• Commerce, especially online electronic commerce


• Finance, for example share prices on stock markets, all managed electronically


• Insurance, all the premiums, incidents, actuarial transactions in an insurance company


• Biomedicine, especially in genetics, where information is literally exploding as gene-sequencing reveals and codes the total genetic profile of a person


• Transport, for example in the airline industry, all the flights, all the passengers


• Climate data, measurements from tens of thousands of weather stations across the World

As also understood from the list given, the correct answer is I, II, III, IV, V and VI.

There is no absolute zero in the temperature measurements. So body temperature is a continuous interval-scale variable.

Grades like "AA" or "BB" taken from a course is an ordinal categorical variable.

Age is an interval-scale variable: to compare two children of ages 10 and 12, we would compute the interval difference, i.e. 2 years. We would not say the 12-year old is 20% older than the 10-year old.

The subjects or individuals or companies on which these observations are made are called cases. Hospital patients on whom we have measured the standard medical indicators such as blood pressure, cholesterol levels, blood sugar, and so on might be cases.

A national sport committe decides to impose stricter conditions for athletes to enter the tournament. One year later, athletes obtain scores that are in general better than those the year before. The increase in scores might be attributable to the introduction of stricter entry conditions.

Bigdata are mostly used in following areas:

• Commerce (especially online electronic commerce),
• Biomedicine (especially in genetics), climate data,
• Transport (for example in the airline industry)
• Climate data
• Insurance
• Finance

Gender is a nominal categorical variable.

Big Data are all forms of data we have obtained from different sources and transformed into meaningful and workable forms. All data that are very large numbers, every value in the billions and trillions do not constitute "big data" because cannot be transformed into meaningful and workable forms.

The paragraph is an example of experimental design in sports science. So, drinking coffee and water on pre-trainings appears to be beneficial to athletes. More experiments with more athletes should be undertaken in other conditions and controls.

Statistics mainly work on numerical data. Qualitative inquiry is not an interest of statistics.

Data come in the form of numbers as well as text, which is something we are discovering more and more. We live in an information world and information is the virtual gold of our society. All observations can be reduced to some numerical quantity, and there are even fields of digital philosophy and digital sociology. It may seem that everything may be recorded and stored somewhere. But in reality - unless we somehow centralize and link all the databases in the world, and have free access to them - we can get access to only a small part of whatever data we are interested in.

A reasonable survey would carefully select people as a sample and gathering data from Denizli would not represent Turkey. Statistics working on samples cannot give absolutely correct information. Sample being carefully selected, it can give a statistical estimate of proportions.

Data such as gender, hometown and branch do not represent a computational score. They can only be used to categorize the sample.

Data labeled as successful, average and unsuccessful does not represent a computational score but does represent an order. So, it is an ordinal data.

Temperature in Celsius does not have an absolute zero. Talking about 20 degrees and 40 degrees, you cannot say its twice warmer but you can say it is 20 degrees more/less. So, the measurement is interval.

Because the difference in the counts of words could be stated in percentages, it is a ratio type of measurement.

In order to prove whether tea has an effect on heart diseases, an experiment needs to be conducted where conditions are controlled.

When it comes to Data Science, however, the term does have some different meaning. Data Science is a field that includes Statistics as well as areas such as Computer Science, Database Management and Data Visualization, for example, and has come into being mainly as a result of the spectacular growth in the amount of available data in this new information world that we live in. The need has been recognized for someone who not only has statistical skills, but also advanced programming skills and knowledge about handling huge data sets, the so-called “Big Data” of today.

What are the “big data” sets today and where do they come from? These are mostly found in the following areas:
• Commerce, especially online electronic commerce
• Finance, for example share prices on stock markets, all managed electronically
• Insurance, all the premiums, incidents, actuarial transactions in an insurance company
• Biomedicine, especially in genetics, where information is literally exploding as gene-sequencing reveals and codes the total genetic profile of a person
• Transport, for example in the airline industry, all the flights, all the passengers
• Climate data, measurements from tens of thousands of weather stations across the world

These terms are the basic concepts in statistics which are population, sample and data.
A population data set contains all members of a specified group (the entire list of possible data values). In this question, 56 million people show the entire population that can vote in the country.
A sample contains a part, or a subset, of a population. The size of a sample is always less than the size of the population from which it is taken. Here, instead of asking 56 million people in the process of public opinion poll, 2000 people selected from the entire population. We call this groups as sample.
Data are individual pieces of factual information recorded and used for the purpose of analysis. These 2000 people were asked which party they would vote for and their responses were recorded as data.

This is a ratio measure. It is ratio scale because it has a true value of 0 (no downloads at all). If your answer is incorrect, please review “Measurement Scales” section in the first chapter.

The ordinal scales are categorical variables and typically measures of non-numeric concepts. This type of variable classifies according to rank.

Only II and III are ratio variables.
I- IQ (intelligence scale): This is an interval variable. It has values of equal intervals that mean something but there is not an absolute zero. In other words, zero IQ does not mean you do not have any cognitive abilities.
II- Weights of newborn babies: This variable is continuous and ratio. It is continuous because weights (kg, g, mg or etc.) can be broken down into very small amounts.Ratio scales have a clear definition of zero.
III-Income earned in a month: It is also ratio because it has an absolute zero and the ratios are meaningful.
IV-Types of blood as A, B, AB and O. This variable is categorical and nominal: The type of blood tells us something meaningful (dominant or recessive genes etc.) but has no meaningful order.
V-Level of Agreement: yes, maybe, no. This is an ordinal variable and the order of categories cannot quantify-how much better it is. For example, is the difference between “Yes” and “Maybe” the same as the difference between “Maybe” and “No” ? We can’t say.

This is an ordinal (categorical) scale because ordinal scales provide an information about the order of choices, such as in a people opinion survey.

This is an observational study. Observational research is non-experimental because nothing (variables, participants etc.) is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting.

The pie graph shows a nominal variable.A nominal scale describes a variable with categories that do not have a natural order or ranking. Here, the frequency of the categories of the nominal variable is shown in percentages. Numbers do not indicate that the variable is a numerical variable, it just refers to the frequency of the categories.

It shows an experimental research. Experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between at least two variables. In other words, whether changes in an independent variable (science course design: with field trip or without field trip) cause a change in a dependent variable (science academic achievement). Experiments have two fundamental features. The first is that the researchers manipulate , or systematically vary, or control the level of the independent variable.

Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered. Big data often comes from multiple sources and arrives in multiple formats. Other concepts are old terms commonly used in statistics.