What is it called when you make inferences about a population based on the sample?
Inferential Statistics Definition
Unlike inferential statistics, descriptive statistics simply describes a data set without helping in drawing inferences. In this context, inferential statistics is said to go beyond the descriptive statistics. It is particularly used when it is not possible to examine each data point of the population.
Inferential Statistics Explained You are free to use this image on your website, templates, etc., Please provide us with an attribution linkArticle Link to be Hyperlinked Inferential statistics allows researchers to make generalizations about a population by using a representative sample. However, since one cannot predict the behavior of a population accurately in almost all cases, the results are said to be based on uncertainty. Further, the sampling errorThe sampling error formula is used to calculate statistical error that occurs when the person conducting the test doesn’t select a sample that represents the whole population under consideration. Formula for sampling error = Z x (σ /√n)read more can be observed here. This error occurs if the sample drawn does not represent the entire population. To prevent this error, it is recommended to collect a random sample before applying inferential statistics. Inferential statistics requires logical reasoning to arrive at the results. The procedure of reaching the outcomes is stated as follows:
TypesLet us go through the types of tools used under inferential statistics. #1 – Regression AnalysisIt measures the change in one variable with respect to the other variable. Linear regression is popularly used in inferential statistics. #2 – Hypothesis Testing ModelsIt requires creating the null and alternate hypothesis. Inferences are drawn by considering the critical value, test statistic, and confidence intervalConfidence Interval refers to the degree of uncertainty associated with specific statistics & it is often employed along with the Margin of Error. Confidence Interval = Mean of Sample ± Critical Factor × Standard Deviation of Sample. read more. A hypothesis test can be two-tailed, left-tailed, and right-tailed. The hypothesis testing models consist of the following tools: a) Z-testZ-testZ-test formula is applied hypothesis testing for data with a large sample size. It denotes the value acquired by dividing the population standard deviation from the difference between the sample mean, and the population mean.read more is used when the sample size is greater than or equal to 30 and the data set follows a normal distribution. The population variance is known to the researcher. The formulas are given as follows: Null hypothesis: H0 : μ=μ0 Alternate hypothesis: H1: μ>μ0 where,
b) T-testT-testA T-test is a method to identify whether the means of two groups differ from one another significantly. It is an inferential statistics approach that facilitates the hypothesis testing.read more is used when the sample size is less than 30 and the data set follows a t-distribution. The population variance is not known to the researcher. The formulas are given as follows: Null Hypothesis: H0: μ=μ0 Alternate Hypothesis: H1: μ>μ0 The representations x̄, μ, and n are the same as stated for the z-test. The letter “s” represents the standard deviation of the sample. c) F-testF-testF-test formula is used in order to perform the statistical test that helps the person conducting the test in finding that whether the two population sets that are having the normal distribution of the data points of them have the same standard deviation or not.read more checks whether a difference between the variances of two samples or populations exists or not. The formulas are given as follows: where,d) Confidence intervalIt suggests the range within which the estimate will fall if the test is conducted on the population. When the confidence interval is high, one can state confidently that the sample results reflect the behavior of the population. ExampleLet us consider an example of inferential statistics. Mr. A wants to open a coffee shop in New York, USA. To design the appropriate menu, a survey is conducted on 300 residents with the aim of understanding their tastes and preferences. The survey includes people of different age groups, gender, and income class. After applying the tools of inferential statistics, the results are stated as follows:
With these outcomes, Mr. A is confident that including all the above varieties of coffee will bring diverse customers to his shop. Moreover, Mr. A also wants to add new, innovative flavors to give a rich drinking experience to his customers. Inferential Statistics vs Descriptive StatisticsThe differences between inferential and descriptive statistics are listed as follows:
Frequently Asked Questions (FAQs)1. What is inferential statistics? Inferential statistics allows collecting a representative sample from the population and ascertaining its behavior through analysis. 2. What is inferential statistics in research? In research, inferential statistics is used to study the probable behavior of a population. The inferences are drawn from the available sample data. Once a sample has been chosen, the researcher can apply any tool of inferential statistics depending on the purpose of research. 3. What are the types of inferential statistics? The types of inferential statistics include the following: 4. Why do we use inferential statistics? Inferential statistics is used for the following reasons: Recommended ArticlesThis has been a guide to Inferential Statistics and its definition. Here, we explain its types, examples and when to use it. You can learn more about statistics from the following articles –
What is sample to population inference?The process of using sample statistics to make conclusions about population parameters is known as inferential statistics. In other words, data from a sample are used to make an inference about a population.
What are the 4 types of inferential statistics?The following types of inferential statistics are extensively used and relatively easy to interpret:. One sample test of difference/One sample hypothesis test.. Confidence Interval.. Contingency Tables and Chi Square Statistic.. T-test or Anova.. Pearson Correlation.. Bi-variate Regression.. Multi-variate Regression.. What is the process by which inference about a population is made from sample information?statistical inference. The process of drawing a sample from a population and then carrying out statistical analysis on the sample in order to make conclusions about the entire population is called statistical inference.
What is reasoning from a sample to a population called?Inferential statistics is a way of making inferences about populations based on samples.
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