Hypothesis Testing in Statistics

In summary, the conversation discussed the question of whether the proportion of females treated this year is different from last year's proportion of 0.65. The suggested approach is to use a one-proportion z-test with a null hypothesis of p = 0.65 and a test statistic of Z = (p̂ - 0.65)/√(0.65*0.35/30). The use of the normal distribution is appropriate in this case, but the binomial distribution could be used for very small sample sizes.
  • #1
war485
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Homework Statement



A random sample of 30 rats has 18 females and 12 males. If last year's proportion of females treated was 0.65, do the above data confirm that this year the proportion of females is different than last year?

Homework Equations



p = treated / total
If using standard normal: z = (value - mean) / (standard deviation / squareroot(sample size) )

The Attempt at a Solution



I *think* the null hypothesis is p = 0.65 and the alternative hypothesis is p ≠ 0.65
What I'm stuck on now is I don't know what test statistic to use to find the p value to decide if I should reject the null hypothesis. Not sure if normal or binomial is supposed to be used and how to do the calculations for this.
 
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  • #2
As you can see from this, in most cases the normal is a good approximation to the binomial. Your null hypothesis is p = 0.65, so normal mean = 0.65 * 30 = 19.5 and normal variance = p*(1-p)*30 under the null hypothesis.
 
Last edited:
  • #3
say that if I can't use the normal approximation, how would I do this?
 
  • #4
Why wouldn't you use the the usual test for one proportion?

[tex]
H_0 \colon p = 0.65, \quad H_a \colon p \ne 0.65
[/tex]

with test statistic

[tex]
Z = \frac{\hat p - 0.65}{\sqrt{\dfrac{0.65 \cdot 0.35}{30}}}
[/tex]

Your sample size, together with the number of "successes", will allow you to use the normal distribution for your test.
 
  • #5
I was thinking about using binomial in case the sample size was really really small (small proportions test) but I'll worry about that later.

Thanks for showing me how I was supposed to approach this problem. :)
 

What is a hypothesis in statistics?

A hypothesis in statistics is a statement or prediction about a population or phenomenon that can be tested using data. It is typically formulated as an alternative and null hypothesis, where the alternative hypothesis is what the researcher believes to be true and the null hypothesis is the opposite.

What is the process of hypothesis testing in statistics?

The process of hypothesis testing involves several steps. First, the researcher formulates a null and alternative hypothesis. Then, a sample is collected and data is gathered. Next, statistical tests are used to analyze the data and determine the probability of obtaining the observed results if the null hypothesis is true. If the probability is low enough, the null hypothesis is rejected in favor of the alternative hypothesis.

What is a type I error in hypothesis testing?

A type I error, also known as a false positive, occurs when the null hypothesis is rejected even though it is actually true. This means that the researcher concludes there is a significant relationship or difference between variables when in reality there is not. The probability of making a type I error is denoted by the alpha level, typically set at 0.05.

What is a type II error in hypothesis testing?

A type II error, also known as a false negative, occurs when the null hypothesis is not rejected even though it is actually false. This means that the researcher fails to find a significant relationship or difference between variables when in reality there is one. The probability of making a type II error is denoted by the beta level, typically set at 0.20.

How do you determine the sample size needed for hypothesis testing?

The sample size needed for hypothesis testing depends on several factors, including the desired level of significance, the effect size, and the power of the study. Generally, a larger sample size is needed for a higher level of significance, a smaller effect size, and a higher desired power. There are also statistical formulas and online calculators that can help determine the appropriate sample size for a specific hypothesis test.

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