Do you mean that the upper limit on the type-I error is 3%? How did 3% become 0.033?GabrielN00 said:Homework Statement
Given ##X_1,\dots,X_{100}##, test ##H_0: \lambda=1## against ##H_a: \lambda=4##. The mean ##\bar{X_{100}}=1.5##
(1) Take the decision on 3% level.
(2) Find the p-value
Homework Equations
##t=\frac{\bar{x}-\mu}{s/\sqrt{n}}##
The Attempt at a Solution
The level of significance is ##0.033##. The p-value I need to evaluate ##\frac{\bar{x}-\mu}{s/\sqrt{n}}##, but I am missing ##s##. Is it still possible to have a solution?
Hypothesis testing is used to determine the validity of a proposed hypothesis or research question by collecting and analyzing data. It allows scientists to make conclusions about their theories and determine whether they are supported by the evidence.
The appropriate statistical test for hypothesis testing depends on the research question, the type of data collected, and the number of variables involved. Generally, the choice of statistical test is determined by the type of data (e.g. categorical or continuous) and the number of groups being compared.
A null hypothesis is a statement that assumes there is no significant difference between two or more groups or variables. An alternative hypothesis, on the other hand, suggests that there is a significant difference or relationship between the groups or variables being studied.
The significance level, also known as alpha (α), is the predetermined level of probability that is used to determine whether the results of a study are statistically significant. Typically, a significance level of 0.05 (or 5%) is used, meaning there is a 5% chance that the results are due to chance rather than a true difference or relationship.
No, hypothesis testing cannot prove causation. It can only determine the likelihood of a relationship between variables, but it cannot establish a cause-and-effect relationship. Other factors and variables that were not considered in the study could also be responsible for the observed results.