Probability Concepts

Probability: The likelihood that a threat will exploit a vulnerability. Probability can use a scale of low, medium, and high, assigning percentage values to each.

• Identify tradeoffs between accuracy and precision required by various probability concepts and the effect on your data.

Statistics involves framing questions in a context, then collecting and analyzing data for interpretation. Probability is about chance and fairness with assigned values. Many mathematics instructors teach this discipline in a procedural manner, causing students to miss its essence.

26) The Bayesian analysis can enable one to improve marginal probabilities of the occurrence of an event by gathering additional information.

Define the likelihood that each risk will eventuate – what is the potential for each of the risks to happen?

Each risk can be either a risk opportunity or a risk threat. The probability and impact matrix assess the importance of each risk. The matrix indicate the impact of a risk on a numerical scale if the risk occurs. The probability of a risk occurring is typically scaled with one being least likely to 10 being most likely to happen. This is important in a project, because a high probability is high priority in the project and need to be addressed sooner than risks with less priority.

As a result, individual expectations, experiences, and biases may influence the way that people view probability of financial events or failures. It is possible to understand that human thinking is impacted by the most important things in life for a person. Fear

However, the statistical definition of “confidence” is much different than the conversational definition. In statistics, confidence does not judge how “correct” a

There are an abundance of different types of probability sampling techniques that are utilized to interpret data. For example, simple random sampling, systematic sampling, stratified, disproportionate stratified sampling, and multistage cluster sampling (Horvitz, 1952; Thompson, 1995). Simple random sampling is the foundation to the probability theory that provides the unbiased measuring instruments that are used to establish the parameters of a geographical area, provides measure for error as well as, assurance to the intervals being correlated. Once the parameters of the sampling frame are determined, random categorizing is established to identify each element within the probability model. Once the information is gathered, a process of random selection is established through computer analysis. By doing so, the electronic database automatically launches a random sequence to select chance data then establishes the results through processes of unsystematic selection. However, the down side to simple random sampling is that the electronic data is not always reliable, and to hand pick that random selection is not always possible.

Many of us know that probability can be used with just about everything. It makes a simpler way to infer the outcome for something. Now, in 2015, we benefit from and use probability in our everyday lives. It could just be the fact that you are having an outdoor wedding. You want to check the probability of rain that the meteorologists found so someone’s big day isn’t

Their findings led to the creation of a theory about risky choices known as the Prospect theory. Based on their studies, people perceive outcomes as gains and losses rather than the totality of wealth and welfare. Thus, people place more value on outcomes that are considered certain, relative to outcomes that were only probable. They coined this phenomenon the "certainty effect" which is pertinent to the decision-making process regarding acceptable risk. Regardless of if a risk contributes to gains or to losses the "certainty effect" dictates the response (Kahneman, D., & Tversky, A.

Data are the facts and figures that are collected, analyzed, and summarized for presentation and interpretation. Statistics was developed in the early 19th century as the study of populations, economies, and moral actions and later in that century as the mathematical tool for analyzing such numbers. Examples of Statistics that are used today are Mean, Median ,Mode and Range, Histograms and Stem and leaf plots. Sample survey methods are used to collect data from observational studies, and experimental design methods are used to collect data from experimental studies. The area of descriptive statistics is concerned primarily with methods of presenting and interpreting data using graphs, tables, and numerical summaries. Whenever statisticians use data from a sample., a subset of the population—to make statements about a population, they are performing statistical inference. The population is how many people i'm doing for my project which is 50 people. Estimation and hypothesis testing are procedures used to make statistical conclusions. Probability plays a key role in statistical inference, it is used to provide measures of the quality and precision of the inferences. Some of these methods are used primarily for single-variable studies, while others, such as regression and correlation analysis, are used to make inferences about relationships among two or more variables. A paradox is a statement that, despite apparently sound reasoning from true premises, leads to a self-contradictory or a logically unacceptable conclusion. The purpose of a paradox is to arrest attention and provoke fresh thought. An entertaining example is to determine the probability that in a randomly selected group of n people at least two have the same birthday. If one assumes for simplicity that a year contains 365 days and that each day is equally likely to be the birthday of a randomly selected person, then

A number of years ago in 1978 there was a movie that starred Warren Beatty. It was called Heaven Can Wait. Throughout the film, there was a common theme of probability and outcome. It kept showing how the probability of something happening seemed almost certain however; the outcome was different in many cases than one would have thought it to be.(Beatty, Beatty, & Henry, 1978)

Objective probability refers to the long-run relative frequency of an event based on the assumption of an infinite number of observations and no change in the underlying conditions. Subjective probability is the individual’s personal estimate of the chance of loss.

Concept of risk, risk assessment, risk management and how uncertainty affects the process will be discussed.