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There is a procedure followed in hypothesis testing. The first step involves the stating of both the null and alternate hypothesis. The hypothesis is stated in a manner that they are mutually exclusive. The second stage is the determination of the level of significance (Lehmann, 2004). The higher the level of significance, the higher the precision attained. The sample size is then determined to allow for the collection of data. The size of data will determine the type of test conducted.
After the collection of data, one moves to the stage of data analysis. In this stage, the value of test statistics is calculated e.g. z-value or the t-value (2012). The analysis of the calculated value is made by use of the statistical tables. The table is used to determine if the calculated score falls within the acceptance or rejection region (Lehmann, 2004). The final stage is then made depending on the location of the score. If the calculated value falls in the rejection region, the null hypothesis is rejected implying that there is significant variation in the observation made.
Two kinds of error can be made in hypothesis testing: type one and type two errors. Type one error is that which leads to the rejection of null hypothesis which is true whereas type two error is the error of accepting a false null hypothesis. In conclusion, the stages in hypothesis testing if well followed makes the hypothesis testing easy to comprehend and make statistical decisions. Hypothesis testing is therefore essential in analyzing data to determine its validity and truthfulness hence important in making statistical
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A hypothesis is a claim that is made about a population parameter such as the mean (µ), a proportion (ρ), or standard deviation (σ) (Triola, p. 392). Hypothesis testing, therefore, involves testing claims, statements or assumptions about various properties of a population which is developed for testing purposes.
Student’s name Name of instructor Course Date Question No 1. Experiment Design and Hypothesis Testing The answer to this question is provided in a stepwise manner consistent with the common steps recommended to be followed when testing a hypothesis as explained by Walker and Maddan (275-283) Step 1.Statement of the Hypothesis to be tested: “In a given period of time, 14-year-old students who eat French fries three times a week will gain at least two more pounds than students who eat French fries two times a week”.
A researcher asks whether attending a private high school leads to higher or lower performance on a test of social skills when compared to students attending public schools. A sample of 100 students from a private school produces a mean score of 71.30. The population mean (μ) for students from public high schools is 75.62.
When engaging in hypothesis testing procedures for the population with known standard deviation, a researcher needs to consider the involved standard error. This value is important in determining confidence limits for decision making over the hypothesis
Statistical importance is found when it comes clear that the findings in the samples are likely not to occur if the findings are not present in the case population (Black, 1999).
Hypothesis testing divides into two categories: the null hypothesis and the
During hypothesis testing the researcher allows themselves a confidence level as they are not 100% sure of the event happening (John, 2007).
Hypothesis testing is an essential part of our decision-making procedure that makes individual to anticipate. As a tutor in
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