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PLEASE MAKE COMMENTS IN EACH NAME OF PERSON AS SHOWING, ALSO GIVE EXAMPLE OF THE DISCUSSIONS WITH MATH WITH REFERENCE AS WELL. Week 3-Discussion
Brenda Kyle;
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis? Explain your answer.
I would use the ANOVA for testing's because from what I read on, and it would be the most accurate. The commonplace information structure for use with an ANOVA requires the free factor to be clear cut, and the reaction to be numerical. Concerning your improvement reaction bend, the upgrade would need to be clear cut, either ostensible or ordinal. In light of your depiction of the improvement, I will expect that it is all out ordinal. The reaction that you're trying, regardless of whether it be the estimation of some phenotype or a learning time for 2 distinct creatures, must be numerical.
What would your choice of test depend on? For the test, you select, explain your design and your comparison groups.
The information must be regularly circulated as to the testing appropriation, and the information must show homoskedasticity - having a standard deviation that is indistinguishable from or like that of different gatherings inside the example. An ANOVA must be utilized under a specific arrangement of conditions: It must be sensible to accept that the information was acquired through an irregular example of an enormous populace and that the information is free concerning every one of the people focuses and every class.
Would the hypothesis be directional or non-directional?
I think it could be non-directional. Directionals theories was utilized as past researches recommends about the discoveries on an investigation that goes with an specific course; nonetheless, within the concentrate has said that therapist did not know about some past studies, directional speculation wasn't suitable (Tanner, 2016).
Would the test be one-tailed or two-tailed?
In respect to the improvement reaction bend, you will in all probability need a 2-way ANOVA. The reaction can be ordered by both creature type and treatment, giving you 2 straight out factors. It is proper to utilize an ANOVA with this information, even though I suggest utilizing an elective test on the off chance that you have 2 factors to work with.
What would be the null, and what would be the alternative hypothesis?
I think using ANOVA would be the best solution to solve all the issues. If I were doing the blood pressure pill studies, I would have 2 different groups to research. One group would have the actual blood pressure pill while the other group has the placebo pill. Both groups would not know who had what and then I world survey them all on how they are feeling.
According to the textbook any event, R. A. Fisher (Tanner, 2016) are available within production on the present-day factual examination. Among the twentieth century, he were employed by an agrarian researchers headquarters. Breaking down an impact on the poison along with composts onto the harvest's amounts, that hindered from the autonomous t-tests (Tanner, 2016) were enabled the researcher thinking about just 2 examples at once. Among that push for suit increased examinations, he made an investigation on variance that is called ANOVA (Tanner, 2016).
References
Tanner, D. (2016). Statistics for the Behavioral Social Sciences, 2nd edition. Bridgepoint
Education
Hi Brenda,
First I want to commend you on being the brave student to comment first on the post. Your research design ideas are good; however the ANOVA is not necessary for this analysis. ANOVA is used with 3 or more groups. If you only have two groups, a t-test is appropriate. In addition, every study has both a null and alternative hypothesis. At the conclusion of the experiment, the statistical results determine which hypothesis has been supported.
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Christa Girard
Hello Dr. Lucas,
I understand the question now that I have read your feedback and will add to my post. I was kind of on the same page as Brenda. Thanks for the clarification.
Christa Reply to Comment
Hi Professor,
The null hypothesis were an hypothesis were no measurable. A null hypothesis is a hypothesis that says there is no statistical significance between the two variables. It is usually the hypothesis a researcher or experimenter will try to disprove or discredit. An alternative hypothesis is one that states there is a statistically significant relationship between two variablescentrality between the two factors. It is normally the hypothesis a analyst or experimenter will attempt to invalidate or ruin. An alternativehypothesis is one that states there is a measurably critical connection between two factors (Tanner, 2016).
Brenda Reply to Comment
Hello Brenda!
After using the online tutor I have a better understanding of when to use the different tests. T-tests are used for studies that have two groups, Z-tests are used for studies that have more than 30 groups and ANOVA is used when the number of groups being tested lies between 2 and 30. Originally, I had added a group that was not necessary. I considered having a high dose group, low dose group and placebo group so I had said that ANOVA was the best to use but after reading Dr. Lucas' feedback on your post I now understand that the t-test is best to use for this analysis because you only need the two groups: group that was given the blood pressure medication and the group that was given the placebo.
We will figure this out!!!
Good luck!
Christa Reply to Comment
Hello Class!
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis ? Explain your answer.
For this analysis I would use ANOVA because there would be 3 groups to cover the hypothesis as well as the limitations in the study. The 3 groups consist of a group that receives a high does of the medication, a group that receives a low dose of the medication and a group that receives a placebo.
What would your choice of test depend on? For the test you select, explain your design and your comparison group.
The choice of test would depend on how many groups there are. T-tests require the use of 2 groups, Z-tests require over 30 groups being studied in a group and the use of ANOVA for analysis would be the number of groups totaling in between 2 and 30 groups. For this test I selected using ANOVA for analysis because I am considering the use of three groups being: a high dose of the blood pressure medication having the most effect out of the three groups, a low dose of the medication having somewhat of an effect and a placebo group having little to no effect at all
Would the hypothesis be directional or non-directional?
In the test I would be conducting directional because I am assuming that the amount of medication given will have have an affect on the blood pressure readings after the medication has been given. I am assuming that the group given the higher dose will show the most change in results.
Would the test be one-tailed or two-tailed? This test would be considered to be one-tailed and this is because there will be one outcome which is the medication will have an effect on the blood pressure readings.
What would be the null and what would be the alternative hypothesis?
The null hypothesis would be there is no statistical difference in the results of the three groups. The alternative hypothesis would be that there is a statistical difference within the three groups. The alternative hypothesis could be based on using the t-test analysis with having only two groups: one dose of the blood pressure medication and then the other group only receiving the placebo rather than adding the third group like I did in my study.
Reference:
Tanner, D. (2016). Statistics for the Behavioral & Social Sciences (2nd ed.). San Diego, CA:
Bridgepoint Education, Inc.
ANOVA Analysis
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis? Explain your answer.
Considering the Nature of the question, I would go for the ANOVA test. It is because to analyze the effects of the introduction of a new drug to Bp; there is a possibility of other factors that are likely to affect this test they, therefore, need to be included. In the event, for example, I used a t-test, this would only limit me two variables, and this would pose a problem on how to analyze another variable. However, when ANOVA is used, even if the independent variable is more than two, such as three, all these independent variables can be analyzed. There are indeed some commonalities that exist between ANOVA and t-test, but when we consider doing the analysis that will include more than two variables, then ANOVA stands to be a more robust statistical test to use (Tanner, 2016). Another reason why I would suggest the use of the ANOVA is that, when research or study involves the study of human beings, there is a likelihood of some level of error variance that is expected. In this scenario, t-test or z-test cannot be in a position to take care of the eventualities, and the only statistical test that can be able to address such issue is ANOVA. Lastly, would suggest the use of the ANOVA because, in case of any variations in the problem under study, ANOVA is at a position to address them. Example; A study carried to establish cognitive effects of the Risperidone in Children with Autism and Irritable Behavior; with several variations/independent. However, variables are to be pronounced such as attention effects, spatial working memory, acute safety, and long-term tolerability and many other which can make all ripple from one single question of analysis and all these can be addressed using ANOVA (Aman et la…, 2008).
What would your choice of the test depend on? For the test, you select, explain your design and your comparison groups.
The main factor that would guide me in selecting the test to use would be the size of the population. For example, if the population under study is too large, it would demand that it be divided into subgroups, and this means I will have several samples that will need to be studied. In such a scenario, the test statistics that would use and give and provide accurate answers that can be relied upon would be ANOVA (Tanner, 2016). Perhaps is because ANOVA can be sued where analysis involves more than two independent variables which are not possible is t-test is used.
Would the hypothesis be directional or non-directional?
Considering the type of question that is we have, the most appropriate hypothesis that can be used in this case would be unidirectional. If the new drug is introduced, one question that needs to be asked would be whether it would decrease or increase the blood pressure, and this transforms the hypothesis to be unidirectional.
Would the test be one-tailed or two-tailed?
Considering the above responses to question three, the test statistics that will be sued would be a one-tailed test. Maybe it is because, when we have a unidirectional hypothesis, a one-tailed test is used, and when we have nondirectional hypothesis two-tailed test is applied.
What would be the null, and what would be the alternative hypothesis?
Depending on the response to qustion3-unidirectional hypothesis would be used and question four that one-tailed test would be used and question one that the suggested statistical analysis would be ANOVA, the following would be the hypothesis: Ho: µ0=µ1=µ2 and H1:µ0≤µ1≤µ2, that null hypothesis and alternative hypothesis respectively(Tanner, 2016).
References
Aman, M. G., Hollway, J. A., McDougle, C. J., Scahill, L., Tierney, E., McCracken, J. T., ... & Cronin, P. (2008). Cognitive effects of risperidone in children with autism and irritable behavior. Journal of Child and Adolescent Psychopharmacology, 18(3), 227-236.doi: http://dx.doi.org/10.1089/cap.2007.0133. It is retrieved from the ProQuest database. Tanner, D. (2016). Statistics for the Behavioral and Social Sciences (2nd ed.) San Diego, CA: Bridgepoint Education, Inc. Reply to Comment
Hi Angela,
You correctly noted that when you have more than 2 groups, it is most appropriate to use an ANOVA instead of a t-test. You also noted that it is possible to examine several independent variables at a time. What would be the problem with performing multiple t-tests instead of a single ANOVA?
Hi Angela, really enjoyed your post. I definitely can understand why the ANOVA test would be used. There are many variables to consider especially while trying to answer the effects medicine has on and blood pressure alone. We have to determine the type of people we're examining whether it's male female or both. Health conditions and risk we are looking for and age. With all these different factors mentioned, you would definitely have more than two sample groups. This test would be perfect. What kind of design would you use to compare your data? "One dependent-groups test where the same group is measured twice is called the before/after t test. An alternative is called the matched-pairs t test, where each participant in the first group is matched to someone in the second group who has a similar characteristic", (section7.2, para 3).
References
Tanner, D. (2016). Statistics for the Behavioral & Social Sciences (2nd ed.). San Diego, CA, Retrieved from: Bridgepoint Education, Inc.
Hi Angela!
I enjoyed reading your post very much! It helped me to gain a better understanding of this week's discussion. I like how you pointed out that you chose to use the ANOVA test because of the error of variance that is likely with the study being based on human beings. It would make sense that this would be the only analysis to apply to the study.
Great post!
Christa
A researcher wishes to study the effect of a new drug on blood pressure. Consider and discuss the following questions as you respond:
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis? Explain your answer.
My recommendation would be using a t-test. T-test is a statistical hypothesis test. t-test compares the mean and standard deviation of two samples to see if there is a significant difference between them. T-test is most commonly used Statistical Data Analysis procedure for hypothesis testing since it is straightforward and easy to use. Additionally, it is flexible and adaptable to a broad range of circumstances. T-test is more adaptable than Z-test since Z-test will often require certain conditions to be reliable.
What would your choice of test depend on? For the test you select, explain your design and your comparison groups.
The T-test is a test a of statistically significant difference between two groups. A t-test compares the means of two groups. For example, compare whether new blood pressure drug differs between a control and treated group, between women and men, or any other two groups. The t -test compares one variable (blood pressure) between two groups.
Would the hypothesis be directional or non-directional?
Non-directional hypothesis predicts that two groups will be different; but doesn’t say in what direction. Non-directional hypothesis is used to prove that changing one variable has an effect on another variable. It does not ask whether the effect is positive or negative. Using the scenario provided we were trying to see the effects of a new drug on blood pressure.
Would the test be one-tailed or two-tailed?
Two-tailed hypothesis tests are also known as non-directional because you can test for effects in both directions. When a two-tailed test is performed, the significance level percentage is split between both tails of the distribution. An advantage of two tail test is the ability to detect both positive and negative effects. Two-tailed tests are standard in scientific research where discovering any type of effect is usually of interest to researchers.
What would be the null and what would be the alternative hypothesis?
Null Hypothesis: There is no significant difference between the blood pressure before and after new drug blood pressure treatment; the difference we see in the means of the two groups may be due to chance and sampling error.
Alternative hypothesis: There is a significant difference between the blood pressure before and after new drug blood pressure treatment; the difference we see in the means of the two groups is mostly likely not due to chance or sampling error.
References:
du Prel, J. B., Röhrig, B., Hommel, G., & Blettner, M. (2010). Choosing statistical tests: part 12 of a series on evaluation of scientific publications. Deutsches Arzteblatt International, 107(19), 343–348. doi:10.3238/arztebl.2010.0343
Tanner, D. (2016). Statistics for the Behavioral and Social Sciences (2nd ed.) San Diego, CA: Bridgepoint Education, Inc.Reply to Comment
Hi Ricky, It's nice to see someone else choose the t test. I'm pretty sure I made this assignment, a little bit more complicated than it had to be. I went on to try and solve my own hypothesis. In my sample I use the paired t-test. I had one sample size but three separate measurements. With that being said I used one tailed test. I see that you have three separate samples with possibly three separate three or more measurements. Do you believe that the ANOVA test would be better. According to Tanner (2016)"The difference is that in the independent t test the IV has just two groups, or levels, and ANOVA can accommodate any number of groups more than one",(section 6.1,para 3) This part had me confused on which one to choose. Seemed like a safe bet for small and large groups.
References
Tanner, D. (2016). Statistics for the Behavioral & Social Sciences (2nd ed.). San Diego, CA, Retrived from: Bridgepoint Education, Inc.Reply to Comment
Sharde,
Thank you for your post and your valuable feedback. ANOVA test hypothesis that is appropriate to compare means of a continuous variable in two or more independent comparison groups like in a research studies where there are more than two comparison groups. In a research study to evaluate a new medication for asthma, researcher might compare an experimental medication to a placebo and to a standard treatment (i.e., a medication currently being used. The ANOVA technique applies when there are two or more than two independent groups. The fundamental strategy of ANOVA is to systematically examine variability within groups being compared and also examine variability among the groups being compared.
Tanner, D. (2016). Statistics for the Behavioral and Social Sciences (2nd ed.) San Diego, CA: Bridgepoint Education, Inc.Reply to Comment
In a study of a new drug on blood pressure, I would recommend the researcher using a one-way ANOVA for the analysis because this test would only involve one independent variable that would determine the difference between any number of groups (Tanner, 2016).
The one-way ANOVA test would depend on the population's gender and culture. The comparison group would be three samples of men and women of different cultures from the same population. Preferably they would attend the same doctor's office and all suffer from hypertension. The first group would be administered 5 milligrams of the new drug daily, the second group 10 milligrams, and the third group a placebo pill (all groups would think that they were taking the new drug). The groups will have their blood pressure taken three times a day and recorded for two weeks then bring their results to their next appointment.
The hypothesis would be directional because I would want to predict change or a difference for the better. The test would also be one-tailed because the test would indicate a prediction (Tanner, 2016).
The Null Hypothesis: What effect will this new drug have on hypertension? Is the difference for men as to women and will it be effective for different cultures?
The Alternate Hypothesis: To test whether the new drug will lower each gender and every culture or only some.
Reference
Tanner, D. (2016). Statistics for the Behavioral and Social Sciences (2nd ed.) San Diego, CA: Bridgepoint Education, Inc.Reply to Comment
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis? Explain your answer.
Considering a study that a researcher wishes to conduct about the effects of a new blood pressure medicine, I would choose to conduct an analysis of variance (ANOVA). Unlike a t-test, an ANOVA can be utilized to compare two or more groups (usually at least three groups). An ANOVA is used to test a research hypothesis and the null hypothesis. An ANOVA is also used to compare between groups variability to within groups variability. Statistics Articles (n.d.) notes that between groups variability is variability from one group to another and within groups variability is variability within one population. An ANOVA is used to measure the sum of squares which simply means, the sum of the squared values. A researcher would get the same results if they conducted multiple t-tests. However, an ANOVA not only compares the means, it examines the variation when calculating the means.
What would your choice of test depend on? For the test you select, explain your design and your comparison groups.
I chose an ANOVA because I find that three groups would be necessary for this type of study. The three groups involved would need to be taken from a population with the same mean. The three groups in the test would have a group receiving the recommended dosage, the group receiving a small dosage, and a group receiving a placebo that does not affect blood pressure one way or another. After completing the study and computing the F ratio (treatment variance divided by error variance), the difference among the group will be discovered (Tanner, 2016). The larger the F ratio, the more likely the differences between the groups are less random. If the F ratio is small, it would indicate that the medicine may not work that well. Tanner (2016) reported that after the ANOVA is completed, the results indicate the sources of variance, the sum of squares values, the degrees of freedom, the mean squares values, and F.
Would the hypothesis be directional or non-directional?
The hypothesis would be considered directional because the researcher would make a prediction regarding a positive or negative effect (Directional Hypothesis, 2012). The entire idea of this study is to understand if the medication affects the sample from the population's blood pressure. Keywords that indicate a directional hypothesis are higher, lower, increase, decrease, positive, or negative (Directional Hypothesis, 2012). If a non-directional hypothesis was chosen, the researcher would not predict the kind of effect that occurs, but that there is, in fact, some sort of effect. Would the test be one-tailed or two-tailed?
Since the researcher wants to measure one direction, a one-tailed test would be used to test the hypothesis. A one-tailed test is used to determine whether the sample mean is higher or lower than populations mean. The results can indicate if the medication has either a positive or negative effect on the sample, but not both like a two-tailed test could.
What would be the null and what would be the alternative hypothesis?
In an ANOVA, the null hypothesis indicates that three samples were drawn from the population with the same mean (Tanner, 2016). The alternative hypothesis changes a bit because three groups do not have one potential alternative. Tanner (2016) stated that sample one population mean value differs from the other samples, sample one and two represent a different mean than the population from sample three, and sample one and three represent a population with a mean value different from sample 2.
References
Directional Hypothesis. (2012, December). Retrieved from http://methods.sagepub.com/reference/encyc-of-research-design/n114.xml
Statistics Articles. (n.d.). Retrieved from https://www.statisticstutors.com/statistics-ANOVA.html
Tanner, D. (2016). Statistics for the Behavioral and Social Sciences (2nd ed.) San Diego, CA: Bridgepoint Education, Inc. Reply to Comment
A researcher wishes to study the effect of a new drug on blood pressure.
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis? Explain your answer .
To study the effect of a new drug on blood pressure I would use an ANOVA. An ANOVA is also known as an analysis of variance and can accommodate any number of groups more than one (Tanner, 2016). Using this statistical method would be the most helpful since it allows researchers to answer some of the same questions a t-test would, like if there is a difference in the effects of the new drug. But the t-test wouldn’t be the best method to use since there is room for error which is about 5% on average and only increases the more test are added. So, using multiple t-test is therefore not a good option (Tanner,2016).
What would your choice of test depend on? For the test you select, explain your design and your comparison groups.
It would depend on the number of groups/variables included in the study. If there are two or less groups, I would use the t-test to explain and compare. If there are three or more groups, I would then use the recommended ANOVA since any number of groups could be used and tested. To me the ANOVA method is more realistic when conducting a research study since a bigger sample size equals reliable results. Sample size is something I would look for before accepting the effects of the blood pressure medicine presented by researchers.
Would the hypothesis be directional or non-directional? Would the test be one-tailed or two-tailed?
Since I am using an ANOVA the, hypothesis would be non-directional. There was no specific direction researchers stated they wanted the test to go so we should be aware the reasoning is due to researchers not being unaware of the effects of the blood pressure medicine; in this case the test would need to be two-tailed. Plus, there was no specific prediction given about the direction or differences in the hypothesis tested. So, because of the vagueness and to be on the safe side I would stick with two tailed test to show the relationship no matter direction.
What would be the null and what would be the alternative hypothesis?
Null hypothesis- Blood pressure is not effected with the use of this new drug designed to treat blood pressure for any of the participants.
Alternative hypothesis- Blood pressure for more than half of the participants were positively affected by using the new drug.
According to our textbook the null hypothesis predicts that the result will not be statistically significant: that there is no(null) difference between the population that the sample represents and the population to which it is compared. The alternate hypothesis predicts that the result will be statistically significant: that there is a difference (Tanner, 2016).
Reference
Tanner, D. (2016). Statistics for the Behavioral & Social Sciences (2nd ed.). San Diego, CA: Bridgepoint Education, Inc Reply to Comment
Hello Shanara!
I was very confused on the directional/non-directional part of this question so I used the online tutor and they explained it to me as the if your hypothesis was basically looking for an outcome then this would be direction, setting a direction for your hypothesis to go in. I was stating that I thought the medication would have an effect so I chose that it would be directional. Your post also makes sense tho because we are not sure what is going to happen.
I'll figure this out eventually!
ChristaReply to Comment
Hi Shanara,
I enjoyed reading your post and thought you did a very nice job presenting your information. I took some additional knowledge away from your post so that is always a plus as well. I didn’t realize that errors raised when additional t-tests were added. I also selected an analysis of variance (ANOVA) due to similar reasoning. However, I chose my hypothesis to be one-tailed and directional due to the subject matter. I think this is because we perceived the questions a bit differently. The way you state the information made complete sense to me, but I would have still stuck with my original choice. I also agree with your take on the null and alternative hypothesis. Great work and good luck with the rest of this week.
Joe Reply to Comment
A researcher wishes to study the effect of a new drug on blood pressure. Consider and discuss the following questions as you respond:
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis? Explain your answer.
I would say that they should either use ANOVA or a t-test for the analysis depending on how many groups they are comparing. If it is only one or two groups, then they should use the t-test; but if they are comparing three or more groups, then they should use ANOVA (Tanner, 2016). The question does not mention the amount of groups that will be compared so I will assume that there will be just one or two groups and therefor a t-test should be used.
What would your choice of test depend on? For the test you select, explain your design and your comparison groups.
The t-test will help get the means from the groups so we can compare them (Tanner, 2016). Using the t-test will help compare the variable of the blood pressure between the groups of the study.
Would the hypothesis be directional or non-directional?
I think the hypothesis would be non-directional.
Would the test be one-tailed or two-tailed?
I think the test would be two-tailed.
What would be the null and what would be the alternative hypothesis?
The null hypothesis would be that after the new drug, there isn't really much of a difference in the blood pressure and any difference shown would be because of sampling error. The alternative hypothesis would be that there is a big difference in the blood pressure after the new drug and any difference shown is not because of sampling error, but because the drug worked. Reply to Comment
Reference:
Tanner, D. (2016). Statistics for the Behavioral & Social Sciences (2nd ed.). San Diego, CA: Bridgepoint Education, Inc.
Rickey Gray
Esther,
I enjoyed reading your post and I must admit when completing my discussion response I was undecided about which test to select. After reading the course material I selected the t-test. The independent sample t-test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. The independent sample t-test can only compare the means for two (and only two) groups. It cannot make comparisons among more than two groups. If you wish to compare the means across more than two groups, you will likely want to run an ANOVA.
Tanner, D. (2016). Statistics for the Behavioral and Social Sciences (2nd ed.) San Diego, CA: Bridgepoint Education, Inc.
Rickey Reply to Comment
Variables The type of test we choose depended on the type of data we collect, the group size and independent/dependent variables. We want to know if a new drug has an effect on blood pressure. Blood pressure is categorized as continuous data because blood pressure rates vary based on the person and activities. The independent variable in this study would be the new drug and the dependent variable blood pressure.
Hypothesis, one-tailed, Null and Alternative The initial aim states "A researcher wishes to study the effect of a new drug on blood pressure". " Effect" and "on" are the keywords that let me know that this statement is non direction. Another word that would indicate nondirectionally is "difference".
My hypothesis - New medication has an effect on blood pressure because it decreases the blood pressure. This hypothesis is directional so I would us one-tailed test.
Null hypothesis: The mean difference is less than or equal to 0 Alternative hypothesis: The mean difference is not less than or equal 0
Test and Design
If I have three people that all took the same new medication but their blood pressures were all different, I would have one sample (groups) and three measurements. Because I am comparing three separate measurements I would use a paired t-test. I would also use the before and after design.
Let say it a 30-day trial. I will take each blood pressure to collect a baseline before the medication and take another blood pressure 30 days after starting medication. In this study, I only measured the diastolic number because it gives an indication of heart attack or stroke risk when the numbers are high.
1. 160 after 150= 10 2. 145 after 134=11 3. 133 after 125=8 Mean difference =29÷3= 9.6
Because the mean difference is in more than 0 my hypothesis is true.
My one sample group consist of three women only. So my hypothesis would state new medication has an effect on women's blood pressure because it decreases their blood pressure.
Hello Shardae!
I also chose that my test would be directional as my hypothesis was that the medication would have an effect on the blood pressure readings. In other terms, I was basically stating that I was expecting this outcome to be achieved.
Thanks for sharing! Reply to Comment
A researcher wishes to study the effect of a new drug on blood pressure. Consider and discuss the following questions as you respond:
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis? Explain your answer.
I would recommend using an ANOVA for the analysis. ANOVA allows the researchers to compare more groups, comparing the amount of variation between groups with the amount of variation within groups.
What would your choice of test depend on? For the test you select, explain your design and your comparison groups.
Size and the desired outcome would help to determine the testing method used. When conducting this study, I would divide the participants into groups depending on age, stress level, and sex. It would be important to monitor the effects of the medication both negative and positive.
Would the hypothesis be directional or non-directional?
The hypothesis would be non-directional. The question asked by the researcher’s askes if the “independent variable will have an effect on the dependent variable, but the direction of the effect is not specified” (Mcleod, n.d.)
Would the test be one-tailed or two-tailed?
This would be a two-tailed test. Because the study is being conducted to study the effect of a new drug on blood pressure it is important to know the effect in both directions. It is important to log the effects, both good and bad, of this new drug. Using statistical tests inappropriately can lead to invalid results that are not replicable and highly questionable (HOME, n.d.).
What would be the null and what would be the alternative hypothesis?
The null hypothesis could be that all of the individuals participating in the study are white males, fair health, ages 40-50. The alternative hypothesis could be that the groups tested are as follows: females, high BP, age 55-60; males, low BP, 45-50; males, moderate BP, 40-45.
References
HOME. (n.d.). Retrieved June 20, 2019, from https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/ (Links to an external site.)Links to an external site.
Mcleod, S. (n.d.). What is a hypothesis? Retrieved June 20, 2019, from https://www.simplypsychology.org/what-is-a-hypotheses.htmlReply to Comment
When evaluating the question regarding a researcher wanting to conduct a study to determine the effects of a new drug on blood pressure; I would recommend using the independent t-test for analysis since it determines if two samples are from the same populations and if their mean is the same (Tanner, 2016). This method of analysis will reveal if the new blood pressure treatment that was given to one group had a different effect on their blood pressure when compared to the second group.
My choice of test would depend on the hypothesis being tested, sample sizes or groups needed, and how the results would represent the population. I started by determining what the researcher was trying to prove, and I concluded that he/she was trying to investigate whether the new blood pressure medication lowered or increased the participant's results to show that there are significant differences between the two groups data due to the medication. To test this hypothesis, I believe that the study would consist of one group with individuals who would take the new drug or independent variable and a second group who would take the previous method of treatment that was already studied. Doing so would measure changes in blood pressure levels between groups to see if there were increases or decreases to values. Since this research contains two groups used to measure the distribution differences for the population, the independent t-test is essential (Tanner, 2016). This test would also establish the standard errors and variability between both groups (Tanner, 2016).
The hypothesis would be directional since it offers an alternate prediction when introducing the independent variable (Tanner, 2016). Because this test is directional, it would be one-tailed; a one-tailed t-test measures how one group varies from the other, to test the range of difference based on the prediction (Tanner, 2016). Therefore, if the hypothesis was to show if the new medication made the blood pressure reading lower or higher than the other sample group, a directional one-tailed test is used to show that the distribution is one-sided, meaning either an increase or decrease in blood pressure levels will occur, but not both. It fits what I believe the researchers are trying to prove.
A null hypothesis is used to show a lack of statistical significance (Tanner, 2016). Knowing this I believe that the null hypothesis for this analysis would be that the new medication had no impact on blood pressure levels in the sample group, that no change was noted to the population mean when introducing this new variable, it remained the same. In contrast, since the alternative hypothesis tries to prove the opposite of the null to show differences in a population (Tanner, 2016). It would be that the new blood pressure medication caused levels for the population mean to change, resulting in an increase or decrease readings. Overall, for this analysis, I believe that an alternate hypothesis would be presented because I selected a directional one-tailed independent t-test. According to Tanner (2016), if the test is “one-tailed, the alternative hypothesis indicates the direction of the predicted difference” (Section 5.4, “The Independent t Test,” para.11). This outcome is what the researcher is measuring — the differences between the means after implementing the new blood pressure medication for that population.
Reference
Tanner, D. (2016). Statistics for the Behavioral and Social Sciences (2nded.) [Electronic version]. Retrieved from https://content.ashford.edu/books/AUPSY325.16.
In this case the t test can be of very important use. The t test is another form of the ANOVA tests. This is because we might have two or more means that we will compare (Tanner, 2016). The first mean is the mean of the symptoms that are presented by the patients with high blood pressure. The second mean is the mean of values and symptoms of the group of people with the high blood pressure who have used the drug. Therefore, to determine whether there is a significant difference in the characteristics between before the patients used the drugs and the mean of characteristic of the patients after they took the drug. In the situation of effect of drugs, we have to use the mean of symptoms because every patient will react differently to the drug.
For this test, I would have groups of comparison. We are looking to use the drugs to ensure that patients’ characteristics and symptoms are moving from those of the disease and moving close to those of people without the disease. Therefore, once the patients have taken the drugs, we look to see whether the patients are moving away from the mean of symptoms of patients with high blood pressure and moving towards the mean of conditions of people without high blood pressure.
The hypothesis in this situation would be directional. This is because the researcher has their own expectation on the drug to work. The researcher will look to ensure that the drug will work. Therefore, this test will have a have a directional hypothesis. The test in this case would be two tailed. This is because the drug can either lead to better results in the patient or even in a worse situation. The test should therefore be two tailed as it will look to see if the effects of the drug will be positive or negative.
H0 : The drug reduces the effects of high blood pressure on the patient
Ha : The drug does not reduce the effects of high blood pressure on the patient
References
Tanner, D. (2016). Statistics for the Behavioral Social Sciences, 2nd edition. Bridgepoint
EducationReply to Comment
Yolanda Bias
Greetings Professor and classmates, I found this discussion question to be very difficult but here are my responses to the week discussion. I welcome all feedback that will help give me a better understanding.
A researcher wishes to study the effect of a new drug on blood pressure. Consider and discuss the following questions as you respond:
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis? Explain your answer.
After reading through all of the choices and doing some additional research I would choose ANOVA for testing. I am not 100% sure of this because I do believe that all of the options could test for the effects of the new medicine. My reason for choosing ANOVA is because it can accommodate any number of groups. More than one group would need to be identified in this study. One group would be given the drug and the other group would continue to take their prescribed blood pressure medicine.
What would your choice of test depend on? For the test you select, explain your design and your comparison groups.
My choice of test depends on my population and being able to measure all of the effects of the new drug as well as this differs from what they were taking. I would have participants taking the new drug and some still taking the prior prescribed medicine to test out the difference. I believe this will allow me to see if the new blood pressure medicine has a better effect than the prior medicine.
Would the hypothesis be directional or non-directional?
I believe it will be directional. It will be measured against the prior medicines to see which works best. It will predict how the medicine from the first group is different from that of the second.
Would the test be one-tailed or two-tailed?
I believe the test would be a one tailed test. Because it will indicate a prediction of how the sample is expected to differ from the population, rather than just assuming that the new blood pressure medicine will work.
What would be the null and what would be the alternative hypothesis?
The null would be that both the prior blood pressure medicine and the new blood pressure medicine have the same effect on all individuals. The alternative hypothesis is that the new blood pressure medicine showed that it works better than the prior. The new blood pressure medicine is more effective in helping to manage blood pressure than the prior medicine used.
I had a rough time with this discussion and would really appreciate feedback on where I went wrong or if I am on the right track. Thanks everyone.
References Tanner, D. (2016). Statistics for the Behavioral Social Sciences, 2nd edition. Bridgepoint Education Reply to Comment
Hi Yolanda,
Nice job on your discussion prompt. I do not truly understand everything either but with additional supports from internet sites and videos on YouTube, I have been able to develop a better understanding. This seems to be a tough subject for a lot of us to grasp so just know that you are not alone in the struggle. I also chose an ANOVA. You could conduct multiple t-tests as well. With an ANOVA, you would need to conduct a post-hoc analysis after F was deemed significant. I also chose a one-tailed, directional hypothesis. Nice job and keep up the good work.
Joe Reply to Comment
ANOVA is used to compare means between three or more groups. Using the t-test produces the possibility of sampling error, and you have to have a sample of less than thirty (<30). I would recommend the z-test in this particular study. To close the margin of error, you would need a sample greater than thirty (n>30). “Z test indicates how distant a sample mean is from the mean of the distribution of sample means in units of the standard error of the mean. When the value of z is 1.96 or greater, there is a probability of p = 0.05 or less that the sample belongs to the population” (Tanner, 2016, Glossary). If you are going to test a new drug to see how it affects blood pressure, you will need a sample sufficient enough to compare to a distinct population. For the test selected, I would take a population of one hundred men and women, all with high blood pressure, dividing them into two groups, and giving one group (1) the “real” pill and the other group (2) a placebo. The hypothesis would be directional and one-tailed because they only want to see the effects of the drug; does it work, or does it not work. The null hypothesis is this drug works by lowering blood pressure. The alternative hypothesis is it seems to work only on women. Reference Tanner, D. (2016). Statistics for the Behavioral & Social Sciences (2nd ed.). San Diego, CA: Bridgepoint Education, Inc. Reply to Comment
Would you recommend using a z-test, a t-test, or an ANOVA for the analysis? Explain your answer.
When looking at a study about the effects of a drug on blood pressure, it can use a different method to analyze the data. After looking at z-test, t-test and ANOVA as options, I have come to believe the best option is ANOVA because it can handle comparing multiple groups. It is a method of analyzing the variance between groups within the samples. It basically takes a statistical test like the one presented and looking to see if the population means are equal. For this analysis, the researchers could run multiple t-tests but that is not very practical. It is important that when using ANOVA, that the data is normally distributed or cleared any normality test.
What would your choice of test depend on? For the test you select, explain your design and your comparison groups.
ANOVA would depend on the difference between means for 2 or more groups. theo samples are equal, large, and normal. If the groups only had 2 or less, then the t-test would be a better fit. The only way a t-test would really benefit the data is if groups are independent from each other. I believe that the research of the new drug will need multiple groups to really find accurate results for this statistical testing. The ANOVA test also depends on the F ratio because it looks at the difference in the different groups making sure the data is not random.
Would the hypothesis be directional or non-directional?
The hypothesis would be non-directional because it predicts the independent variable and how that impacts the dependent variable. Directional Hypothesis may state how the results may be predicted. However, non-directional will state that the different groups may have different results that may result in significant differences at the end. The research of drugs impacting blood pressure is assumed to have multiple impacting data.
Would the test be one-tailed or two-tailed?
Since non-directional hypothesis is used, a two-tailed test is used because both sides of the rejection can happen on both sides. This is important when the critical area of the distribution is 2-sided. It is also impacted when the samples are greater or less than range of values within a normal distribution.
Alternative hypothesis, in contrary to the null hypothesis, observations are resulted in a real effect. It is also known as a maintained hypothesis. This would mean that their would be difference between the blood pressure and the new drug being implemented by any error in the sample.
References:
Tanner, D. (2016). Statistics for the Behavioral Social Sciences, 2nd edition. Bridgepoint
Hi Brian,
Great job on your discussion post. I also chose an ANOVA due to similar reasoning. Multiple t-tests would be impractical but with an ANOVA, a post-hoc test would be necessary to determine where the differences occurred between each sample group. I chose a directional, one-tailed hypothesis but your explanation seems accurate with the way you perceived the questions. I am anxious to find out if I am correct in my reasoning. I think that I am, but your explanation has me doubting my reasoning a bit. Good luck with the rest of this week's work and keep up the good work.
Joe