Creative Assignment: Hypothesis Testing - Key Concepts Explained

This creative assignment focuses on explaining hypothesis testing and related concepts. It defines the null and alternative hypotheses, differentiating between one-sided and two-sided alternatives. The assignment also clarifies the concept of the p-value and its role in determining statistical significance. It further discusses the effect size and its importance in assessing the impact on the test sample. The explanation covers Type I and Type II errors, defining when each occurs in relation to accepting or rejecting the null hypothesis. Finally, the assignment defines significance level and power, providing a comprehensive overview of hypothesis testing within the context of statistical analysis. Desklib offers a wealth of similar solved assignments and past papers to aid students in their studies.

Running head: CREATIVE ASSINGMENT
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Skills Assignment A2
Hypothesis testing
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CREATIVE ASSIGNMENT
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Selected statistical topic: Hypothesis tests and the confidence interval
Hypothesis tests are crucial to research studies in evaluating two mutually exclusive
statements about a population of interest to establish which one is supported by the sample data.
They can be performed using t-tests or Chi-Square statistical approach run on SPSS or MS Excel
among other techniques. A null hypothesis is a statement in which the researcher seeks to
discredit or disapprove that has no statistical significance whereas an alternative hypothesis is a
statement, which carries a statistically significant association between a set of variables. An
alternative hypothesis can be a one-sided alternative, which depicts that a variable is either
smaller or larger than the null hypothesis contrary to a two-sided alternative, which deduces
that a sample size or a certain variable is not equal to that of the null hypothesis. On the other
hand, the probability of determining the deviation from the null hypothesis is referred to
statistical significance referred to as the p-value, which determines whether the null hypothesis
would be rejected or accepted, in essence, when p is higher than 0.05, H0 would be dismissed and
when P is lower than 0.05, H0 is accepted. The value 0.05 is regarded as the significance level,
which is the probability of rejecting the null hypothesis and correspondingly implies that the
level of confidence is 95%. Concisely, the effect size is a measure of the impact on the test
sample that makes a relative comparison to determine the type of error generated. The type 1
error is returned when the H0 is rejected when true whereas type 11 error is returned when the
test fails to reject the H0 when false. Moreover, power is the opposite of type 11 error, which is
the probability of determining a treatment effect in the presence of a single treatment.