C1 Overview and Introduction

• Nominal scales Values on a nominal scale merely“name”the category to which the object under study belongs.
• Ordinal scales can be “ordered” to reflect differing degrees or amounts of the characteristic under study.
• Interval scales Values on an interval scale overcome the basic limitation of the ordinal scale by having“equal intervals.”
• Ratio scales has the features of an interval scale and it permits ratio statements, ratio scales have an absolute zero.
• The mode is the most frequently occurring value in a distribution.
• The median is the middle value when the data are arranged from smallest to largest.
• Mean (or average) The mean is calculated as the sum of all numbers divided by the number of items being averaged.
• Range The range is simply the difference between the highest and lowest values in a dataset.
• A variable is a characteristic (of a person, place, or thing) that takes on different values.
• Measurement is the process of assigning numbers to the characteristics you want to study.
• statistical conclusion: conclusion about the statistical question you raised.
• A statistical question is a question that can be answered by collecting data that vary. For example, “How old am I?” is not a statistical question, but “How old are the students in my school?” is a statistical question.
• Inferential statistics permit conclusions about a population, based on the characteristics of a sample of the population.
• Descriptive statistics describe the characteristics of a set of observations. Descriptive statistics include measures such as mean, median, mode, standard deviation, variance, quartiles, percentile, etc.
• Ordinal Scale is defined as a variable measurement scale used to simply depict the order of variables and not the difference between each variable.
• Nominal Scale is defined as the simplest form of categorizing data into groups without any ordering or ranking involved. It is also called Categorical Data
• Statistical inference is a method of making decisions about the parameters of a population, based on random sampling. It helps to assess the relationship between the dependent and independent variables. The purpose of statistical inference to estimate the uncertainty or sample to sample variation.
• Hypothesis testing refers to a process used by analysts to assess the plausibility of a hypothesis by using sample data. In hypothesis testing, statisticians formulate two hypotheses: the null hypothesis and the alternative hypothesis.
• The null hypothesis (H0) states that there is no significant difference between the means of two populations. This is usually denoted with an equal sign (=).
• Type I Error occurs when we reject the null hypothesis when it is true. Also known as false positive error.
• Alternative Hypothesis (Ha): States that there is a significant difference between the means of two populations. Usually denoted with a less than symbol (<)
• Alternative Hypothesis (Ha): States that there is a significant difference between the means of two populations. Usually denoted with a less than symbol (<), greater than symbol (>) or not equal to symbol (!)
• Type II Error occurs when we fail to reject the null hypothesis when it is false. Also known as false negative error.
• Power of Testing is the probability of correctly rejecting the null hypothesis when it is false.