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What is Inferential Statistics? 

 Inferential statistics is a set of procedures used to estimate the probability of one or more events based on samples. Statistical inference is a form of statistical hypothesis testing. The simplest form of inferential statistics uses a sample data set to draw conclusions about the population from which it was drawn. Researchers often use inferences from samples in medical research, engineering, economics, and biology to determine such things as what causes cancer or how often an event occurs in nature. Inferential statistics can also be used for forecasting purposes, predicting the future value for an instrument or product based on past data sets and making decisions about whether or not those predictions will hold true under different hypothetical scenarios.

Types Of Inferential Statistics

Statistic inference: Statistic inference is using statistics to make conclusions about a sample and can be done in many ways. The first way is a confidence interval which is used to estimate population parameters in a certain amount of confidence. The second way statistic inference can be used is by hypothesis testing. Using this method researcher can test if the difference between two groups or the population mean of one group is greater than another group or not based on the observed data.

Non-statistical inference: Non-statistical inference uses logical reasoning to make conclusions about populations, other than statistical methods of doing so. This method would be used if a researcher wanted to test whether alcohol consumption leads to increased traffic accidents or if a researcher wanted to see in people who exercise on the weekends are healthier or not.

Importance Of Inferential Statistics

Inferential statistics is a useful tool to use when it helps to determine how certain events will affect the future. This is significant because inferential statistics can help predict and prevent future events by understanding the behavior of variables that are already known. The use of inferential statistics has the capability to revolutionize modern medicine in that it provides physicians with a tool that allows them to analyze possible outcomes for patients so that they can more effectively and accurately identify which patients have a high likelihood of surviving an upcoming trial and which patients have a low likelihood of surviving.

Currently, medical professionals are using Inferential Statistics in order to better predict cases with any type of ailment within their care, including stroke, cancer and heart disease. As of now, while this process is still underway, doctors are already able to predict the recovery rate of stroke patients with a relatively high degree of accuracy. This means that they can accurately identify which patients will most definitely make a full recovery and which ones might not survive; this is certainly valuable information considering that before inferential statistics were adopted, doctors could only tell you the probability for patients to survive.

With inferential statistics, medical professionals will be able to make better diagnoses for their patients and have a greater understanding of which diseases are more likely to develop from symptoms in the present time. This allows the medical professionals to effectively treat the proper patients from a group of similar patients, allowing them to come up with an accurate diagnosis for a disease even if there hasn’t been a case in a particular area for quite some time. This is also very valuable in that it gives doctors the opportunity to administer treatment as soon as possible rather than waiting until later when the disease has progressed enough that effective treatment isn’t possible anymore.

Topics Studied In Inferential Statistics

  1. Binomial Theorem- provides the formulas for what are called binomial distributions, which describe the probability of drawing heads or tails in each trial.
  2. Hypothesis Testing- provides procedures for testing a hypothesis, such as a null hypothesis and alternative hypotheses, as well as tests and criteria to determine whether the H1 has been rejected or not.
  3. Distributions- describes continuous random variables with many values that do not follow a normal distribution, and how to estimate their parameters using various methods such as chi-squared and t-distributions.
  4. T-Distributions- describes a continuous distribution that is similar to the normal distribution, except having more variance and spreading more.
  5. Central Limit
  6. Confidence Intervals- provides a method for describing how precise we can be in estimating a particular value, based on samples and their numbers of errors from some type of distribution.
  7. Regression Analysis / Linear Regression- describes the procedure for analyzing data using linear equations (straight lines) and least squares calculations to fit them together using various methods such as maximum likelihood estimations, minimal differences estimations or nonlinear methods such as polynomial regression.

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