# Statistics Help : Hypothesis Testing

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• girlwhoneedsmathhelp
In summary, hypothesis testing is a statistical method used to evaluate the validity of a given hypothesis based on sample data. It involves creating a null hypothesis and an alternative hypothesis, collecting data, and using statistical tests to determine the probability of the null hypothesis being true. The difference between a null hypothesis and an alternative hypothesis is that a null hypothesis assumes no significant difference or relationship between variables, while an alternative hypothesis assumes the opposite. Type I and Type II errors are both possible outcomes in hypothesis testing, with Type I error being a false positive and Type II error being a false negative. A p-value is the probability of obtaining results as extreme as the observed results, assuming the null hypothesis is true. It is used to determine the significance of results and whether
girlwhoneedsmathhelp
TL;DR Summary
There's this question and I have worked it out until the end but don't understand why the answer is the answer

I understnad why x(< or = ) 2 but I do not understand why we use 16 instead of 17 for the second range? When P(X>=16) > 0.005(which is the level of significance). Thank you for all the help given :)

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"As close as possible to 0.005".

0.0064 is closer to 0.0050 than 0.0021.

FactChecker
None of this is exact of course but I believe I was taught to default to the more conservative number when rounding to an integer value. This is not what the question prescribes but seems more prudent to me ( the significance level will be 1% or less). I am not sure which is the norm!

FactChecker

## What is hypothesis testing?

Hypothesis testing is a statistical method used to determine if there is enough evidence to support a claim or hypothesis about a population. It involves collecting and analyzing data to make a decision about the validity of the hypothesis.

## What is the difference between a null hypothesis and an alternative hypothesis?

A null hypothesis is a statement that assumes there is no significant difference or relationship between variables in a population. An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables in a population.

## What is a p-value and how is it used in hypothesis testing?

A p-value is the probability of obtaining a result at least as extreme as the observed result, assuming the null hypothesis is true. It is used in hypothesis testing to determine the likelihood of the observed data occurring by chance and to make a decision about the validity of the null hypothesis.

## What is a type I error and a type II error in hypothesis testing?

A type I error occurs when the null hypothesis is rejected, but it is actually true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected, but it is actually false. This is also known as a false negative.

## What are the steps involved in hypothesis testing?

The steps involved in hypothesis testing include: 1) stating the null and alternative hypotheses, 2) selecting an appropriate test statistic, 3) determining the level of significance, 4) collecting and analyzing the data, 5) calculating the p-value, and 6) making a decision to either reject or fail to reject the null hypothesis based on the p-value and level of significance.

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