Testing Statistical Hypothesis Pdf Type I And Type Ii Errors This lecture introduces the t test our first real statistical test and the related t distribution. the t test is used for such things as: determining the likelihood that a sample comes from a population with a specified mean. In hypothesis testing, a type i error means rejecting the null hypothesis when the null hypothesis is true. the probability of a type i error is called (alpha).
Hypothesis Testing Pdf Statistical Significance Hypothesis Identify the four steps of hypothesis testing. define null hypothesis, alternative hypothesis, level of significance, test statistic, p value, and statistical significance. define type i error and type ii error, and identify the type of error that researchers control. calculate the one independent sample z test and interpret the results. This document discusses hypothesis testing for a single sample. it begins by defining statistical hypotheses and the key terms like the null hypothesis, alternative hypothesis, type i and type ii errors. Hypothesis test statistical method that uses sample data to evaluate a hypothesis about a population the logic state a hypothesis about a population, usually concerning a population parameter predict characteristics of a sample obtain a random sample from the population compare obtained data to prediction to see if they are consistent. Enter statistics hypothesis testing formalizes our intuition on this question. it quantifies: in what % of parallel worlds would the results have come out this way? this is what we call a p value. p<.05 intuitively means “a result like this is likely to have come up in at least 95% of parallel worlds” (parallel world = sample).
Testing Of Hypothesis Pdf Statistical Significance Statistical Hypothesis test statistical method that uses sample data to evaluate a hypothesis about a population the logic state a hypothesis about a population, usually concerning a population parameter predict characteristics of a sample obtain a random sample from the population compare obtained data to prediction to see if they are consistent. Enter statistics hypothesis testing formalizes our intuition on this question. it quantifies: in what % of parallel worlds would the results have come out this way? this is what we call a p value. p<.05 intuitively means “a result like this is likely to have come up in at least 95% of parallel worlds” (parallel world = sample). Hypothesis testing is a procedure, based on sample evidence and probability theory, used to determine whether the hypothesis is a reasonable statement and should not be rejected, or is unreasonable and should be rejected. Why do hypothesis testing? sample mean may be di↵erent from the population mean. 1. left tailed test. if p value ↵, we reject h0 and say the data are statistically significant at the level ↵. if p value > ↵, we do not reject h0. 2. right tailed test. if p value ↵, we reject h0 and say the data are statistically significant at the level ↵. Testing a statistical hypothesis deals with how to make a decision between two competing claims (hypotheses). definition: a statistical hypothesis or simply a hypothesis is an assertion or a claim about one or more population characteristics or about the form of population distribution. •test of hypothesis: is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. for example, the null hypothesis should be rejected.
Comments are closed.