Module-1_Basic-Stat-Concept.pdf

University of Antique Unit 1 Laboratory High School Lesson 1: Basic Statistical Concepts Unit 1. Introduction to Statistics...

University of Antique Unit 1 Laboratory High School Lesson 1: Basic Statistical Concepts Unit 1. Introduction to Statistics Lesson 1 – Basic Statistical Concepts Learning Objectives: At the end of the lesson, you should be able to: 1. defined the basic concepts of statistics; 2. give examples of the different levels of measurement; 3. differentiate: a. descriptive vs. inferential statistics b. data vs. variables c. population vs. sample d. independent vs. dependent vs. controlled variables Introduction: As our society becomes more technologically complex, greater demands are being placed on professionals to understand and use the results of research designed to solve applied problems. Basic Concepts in Statistics The word statistics is derived from the Greek word statistiks (Mariappan, 2019). The early use of statistics can be traced from the administration of the state regarding the population and property usually for war and finance purposes. Statistics is defined as the science of collection, organization, presentation and analysis of data. Thus, one can draw conclusion and make reasonable decision based on the analysis of that data. Areas of Statistics There are two broad areas of statistics- the descriptive and inferential. Descriptive statistics can be defined as a set of methods involving the collection, presentation and summarization by means of numerical descriptions. Inferential statistics, on the other hand, is a set of methods that allow 11 estimation or testing of the characteristics of the population based only from the sample drawn from that population. Examples of Descriptive Statistics 1. The weekly mean sales of TV sets in a certain store. 2. Alcohol is the most frequent disinfectant against COVID–19. 3. At least 5% discount is deducted on the online sale. 4. The rice importation was doubled last year compare to the rice importation two years ago. 5. The median age of the College of Business and Accountancy students is 25 years old. Examples of Inferential Statistics 1. Salary predicts the life satisfaction of businessmen in Antique. 2. Productivity of crops is a factor in determining the choice of students to go into farming. 3. Awareness of COVID – 19 symptoms is directly related to resiliency of the residents living in Cebu City. 4. Number of received calls predicts the number of orders in a certain flower shop. Data is used to describe a collection of natural phenomena descriptors, including the result of experience, observation or experiment. These could be numbers, words or images that are used as measurement or result from observations of a set of variables. Data can be drawn from the population or sample. Population is the entirety of Statistics Page | 1 University of Antique Unit 1 Laboratory High School Lesson 1: Basic Statistical Concepts individuals or objects of interest. Sample is a portion or part of the population of interest. The measures of the population are called parameter and the measures of the sample are called statistic or estimates. For example, during the 2020 census the mean age of Antiqueños represents the parameter however if the researcher took sample from the entire population of Antiqueños and then compute the mean age, the value is called estimate. In other words, all values taken from the population is called the parameter while values taken from the sample is called estimates. Slovin’s Formula: 𝑁 𝑛= (1 + 𝑁𝑒 2 ) 𝑛 − sample size 𝑁 − population size 𝑒 − margin of error Variable is the characteristics of an individual or object that can be measured. A variable must vary or have different values in the study. For example, sex is a variable because it can have two values, male and female. However, if you are studying the quality of life of seamen in certain area and they are all male, sex cannot be considered as a variable in that study. Levels of Measurement There are four (4) levels of measurement – nominal, ordinal, interval and ratio. 1. Nominal scale objects or individuals are assigned into categories and have no numerical properties. This is the lowest scale. Example: sex (male, female) marital status (single, married) highest educational attainment (elementary, secondary, or college graduate) bath soap brand (Safeguard, Palmolive, Zest, Bioderm, etc.) They are nominal in nature. They do not possess quantitative properties. 2. Ordinal scale objects or individuals form a category and the categories form a rank along continuum. Ordinal data are sometimes referred to as ranked data and they can be arranged in order either descending or ascending. Even the data are ranked; their distances may not be equal. Example: Student’s Academic Performance (1st, 2nd, 3rd, etc.) Shoe size (small, medium, large) Statistics Page | 2 University of Antique Unit 1 Laboratory High School Lesson 1: Basic Statistical Concepts Classroom Officers (Mayor, Vice Mayor, Secretary, etc.) 3. Interval scale objects or individuals include the characteristics of ordinal scale objects in addition to that the differences between the values are a constant size. Another property of an interval scale measurement is that there is no absolute zero. Meaning, zero does not denote the absence quantity being measured. Example: Temperature (since 0℃ does not denote absence of temperature) IQ test (if you got a score of 0 it does not mean you have no IQ) 4. Ratio scale objects or individuals has the characteristics than that of the interval scale but an addition to that is that it has absolute zero. In ratio scale, zero represents nothing. Zero means absence of the quantity being measured. Example: Weight Length Number of students inside the classroom Type of Variables There are two types of variables – qualitative and quantitative. Qualitative variables are variables that can be classified into categories, according to characteristics or attributes. For example, sex is a qualitative variable because you can classify as either male or female. Another example is the color of the eyes (blue, brown, etc). Quantitative variables are variables that are numerical or you can possibly rank them. The examples of quantitative variables are age, number of 13 deliveries, amount of sugar, etc. Quantitative variables can further be classified discrete or continuous. Discrete variables can assume only certain values. Usually, discrete variables are countable. Continuous variables are variables that can assume any values between two values. For example, weight of cargo vessels, time consumed in reading a novel and others. Statistics Page | 3 University of Antique Unit 1 Laboratory High School Lesson 1: Basic Statistical Concepts Discrete vs. Continuous Variables Independent vs. Dependent vs. Controlled Variables Variables are an important part of an eye tracking experiment. A variable is anything that can change or be changed. In other words, it is any factor that can be manipulated, controlled for, or measured in an experiment. Experiments contain different types of variables. We will present you with some of the main types of experimental variables, their definitions and give you examples containing all variable types. Statistics Page | 4 University of Antique Unit 1 Laboratory High School Lesson 1: Basic Statistical Concepts Independent variables (IV): These are the factors or conditions that you manipulate in an experiment. Your hypothesis is that this variable causes a direct effect on the dependent variable. Dependent variables (DV): These are the factor that you observe or measure. As you vary your independent variable you watch what happens to your dependent variable. Controlled (or constant) variables: Are extraneous variables that you manage to keep constant or controlled for during the course of the experiment, as they may have an effect on your dependent variables as well. To better illustrate the relationship of these variables, a figure is presented below. Another example of experimental variables is the extraneous variables. Extraneous variable: An extraneous variable is any extra factor that may influence the outcome of an experiment, even though it is not the focus of the experiment. Ideally, these variables won’t affect the conclusions drawn from the results as a careful experimental design should equally spread influence across your test conditions and stimuli. Nevertheless, extraneous variables should always be considered and controlled when possible as they may introduce unwanted variation in your data. In this case, you need to tweak your design and procedure to be able to keep the variation constant or find a strategy to monitor its influence (constant or controlled variables). All experiments have extraneous variables. Here are some examples of different types of extraneous variables: Statistics Page | 5 University of Antique Unit 1 Laboratory High School Lesson 1: Basic Statistical Concepts o aspects of the environment where the data collection will take place, e.g., room temperature, background noise level, light levels; o differences in participant characteristics (participant variables); and o test operator, or experimenter behavior during the test, i.e., their instructions to the test participants are not consistent or they give unintentional clues of the goal of the experiment to the participants. To further understand the concepts, you can watch this video https://www.youtube.com/watch?v=MXaJ7sa7q-8, Introduction to Statistics (Simple Learning Pro, 2015). 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