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“Future physicians welcome to nstomp on step. 1. The only free videos series that helps helps you study more efficiently by focusing non the highest yield material. I u2019m brian and i will be your guide non this journey through null hypothesis alternative hypothesis type.
I and type ii error. P. Value. Nalpha beta.
Power statistical significance. This is the 11th video in my playlist. Covering nall of biostatistics and epidemiology for the usmle step. 1.
Medical board exam. There is a lot to cover but we will try to nmove through things quickly and break them down into bite sized pieces. We will start with the null hypothesis. Which nis represented by h.
Subscript zero. The null hypothesis states that there no difference nbetween. The groups being studied in other words. There is no relationship between nthe risk factor or treatment being studied and occurrence of the health outcomes for example.
If we are comparing a placebo ngroup to a group. Receiving a new diabetes medication. Then then null hypothesis states nthat. The blood sugars or medical complications would be roughly the same in each group.
We will talk about this more in a second nbut by default you assume the null hypothesis is correct. Until you have enough evidence nto support rejecting this hypothesis. If you are the researcher. It is usually kind nof a bummer when the null hypothesis is valid.
Because it means you didn u2019t find a treatment nthat works or that the risk factor. You are studying isn u2019t. As important as you were nhoping. The alternative hypothesis is denoted by h.
Nsubscript. A or h1 as you might expect. It is the opposite of nthe null hypothesis. This hypothesis states that there is a difference nbetween groups.
The research groups are different with regard nto. What is being studied in other words. There is a relationship between nthe risk factor or treatment and occurrence of the health outcome nobviously. The researcher wants the alternative hypothesis to be true.
If the ha is true it means they discovered na treatment that improves patient outcomes or identified a risk factor that is important nin. The development of a health outcome..
However you never prove the alternative hypothesis nis true you can only reject a hypothesis say. It is nfalse or fail to reject a hypothesis could be true but. You can never be totally sure so a researcher really wants to reject the nnull hypothesis. Because that is as close as they can get to proving the alternative nhypothesis is true in other words you can u2019t prove a given treatment ncaused a change in outcomes.
But you can show that that conclusion is valid by showing that nthe opposite hypothesis or the null hypothesis is highly improbable given your data anytime. You reject a hypothesis. There is a nchance you made a mistake this would mean you rejected a hypothesis nthat is true or failed to reject a hypothesis that is false type. 1.
Error. Is when you incorrectly rejecting nthe null hypothesis. The researcher says. There is a difference nbetween.
The groups when there really isn u2019t. It can be thought of as a false positive study nresult usually we focus on the null hypothesis and ntype 1 error. Because the researchers want to show a difference between groups if there is any intentional or unintentional nbias. It more likely exaggerates the differences between groups based on this desire the probability of making a type.
I error is ncalled alpha. You can remember this by thinking that alpha nis. The first letter in the greek alphabet so it goes with type 1 error. I u2019m gonna hold off on talking about alpha nand p.
Value for a few slides type. 2. Error is when you fail to reject the nnull. When you should have rejected the null hypothesis.
The researcher says. There is no difference nbetween. The groups when there is a real difference. It can be thought of as a false negative study nresult.
The probability of making a type ii. Error. Nis. Called beta.
You can remember this by thinking that u03b2 nis. The second letter in the greek alphabet power is the probability of finding a difference nbetween groups if one truly exists it is the percentage chance that you will nbe able to reject the null hypothesis. If it is really false power can also be thought of as the probability nof not making a type 2 error in equation form power equals 1 minus beta. It is good for a study to have high.
Power a cutoff for differentiating high from low npower. Would be roughly around 08 or. 80 . In other words.
Having a beta. Less than 20 nfor..
A given study is good where power comes into play. Most often is nwhile. The study is being designed before you even start the study you may do npower calculations based on projections that way you can tweak the design of the study nbefore you start it and potentially avoid performing an entire study that has really nlow power since you are unlikely to learn anything power increases as you increase sample size nbecause you have more data from which to make a conclusion power also increases as the effect size or nactual difference between the group u2019s increases. If you are trying to detect a huge difference nbetween groups.
It is a lot easier than detecting a very small difference between groups increasing the precision or decreasing standard ndeviation of your results also increases power. If all of the results. You have are very similar nit is easier to come to a conclusion than if your results are all over the place p value is the probability of obtaining a nresult at least as extreme as the current one assuming that the null hypothesis is ntrue imagine. We did a study comparing a placebo ngroup to a group that received a new blood pressure medication and the mean blood pressure nin.
The treatment group was 20 mm hg lower than the placebo group. Assuming the null hypothesis is correct the np value is the probability that if we repeated the study. The observed difference between nthe group averages would be at least 20. Now you have probably picked up on the fact nthat.
I keep adding the caveat that this definition of the p value. Only holds true if the null nhypothesis is correct aka if. Is no real difference between the groups . However don u2019t let that throw you off you just assume.
This is the case in order nto perform this test. Because we have to start from somewhere. It is not as if you have to prove the null nhypothesis is true before you utilize the p value the p value is a measurement to tell us how nmuch the observed data disagrees with the null hypothesis. When the p value is very small.
There is more ndisagreement of our data with the null hypothesis and we can begin to consider rejecting the nnull hypothesis aka saying there. Is a real difference between the groups being studied in other words. When the p value is very small nour data suggests. It is less likely that the groups being studied are the same therefore when the p value is very low our ndata is incompatible with the null hypothesis and we will reject the null hypothesis.
When the p value is high. There is less disagreement nbetween. Our data and the null hypothesis in other words. When the p value is high it nis more likely that the groups being studied are the same in this scenario.
We will likely fail to reject nthe null hypothesis. You may be wondering what determines. Whether na p. Value is u201clow u201d or u201chigh u201d.
That is where the selected u201clevel of significance u201d nor alpha comes in as we have already discussed alpha is the nprobability of making a type i. Error or the probability of incorrectly rejecting the null nhypothesis . It is a selected cut off point that determines nwhether. We consider a p value acceptably high or low.
If our p value is lower than alpha. We conclude nthat. There is a statistically significant difference between groups when the p value is higher than our significance nlevel. We conclude that the observed difference between groups is not statistically significant alpha is arbitrarily defined a 5 level of significance is most commonly nused in medicine.
Based. Only on the consensus of researchers using a 5 alpha..
Implies. That having a 5 nprobability of incorrectly rejecting the null hypothesis is acceptable. Therefore. Other alphas such as 10 or 1.
Nare. Used in certain situations. So here is the key that you need to understand in most cases in medicine. If the p value nof.
A study is less than 5. Then there is a statistically significant difference between ngroups. If the p value is more than 5 than there nis not a statistically significant difference between groups. There are a couple caveats that complicate nthings a bit both are related to how you can u2019t take statistics nout of context to make conclusions statistical significance is not the same things nas clinical significance clinical significance is the practical importance nof the finding there may be statistically significant difference nbetween.
2 drugs. But the difference is so small that using one over the other is not na big deal for example. You might show a new blood pressure nmedication is a statistically significant improvement over an older drug. But if the nnew drug.
Only lowers blood pressure on average by 1. More mm hg. It won u2019t have a meaningful nimpact on the outcomes that are important to patients. It is also often incorrectly stated by students nresearchers review books etc.
That u201cp value can be used to determine that the observed ndifference between groups is due to chance or random sampling error u201d in other words u201cif. My p value is less than nalpha then there is less than a 5 probability that the null hypothesis is true u201d while this may be easier to understand and nperhaps may even be enough of an understanding to get test questions right. It is a misinterpretation nof p. Value for a number of reasons.
P. Value is a tool. Nthat can only help us determine the observed. Data.
U2019s level of agreement or disagreement nwith. The null hypothesis and cannot necessarily be used for a bigger picture discussion about nwhether our results were caused by random error. The p value alone cannot answer. These larger nquestions in order to make larger conclusions about nresearch results.
You need to also consider additional factors such as the design of the nstudy and the results of other studies on similar topics. It is possible for a study to have a p value nof less than 005. But also be poorly designed and or disagree with all of the available nresearch on the topic. Statistics cannot be viewed in a vacuum.
When nattempting to make conclusions. And the results of a single study can only cast doubt on the nnull hypothesis. If the assumptions made during the design of the study are true a simple way to illustrate this is to remember nthat by definition. The p value is calculated using the assumption that the null hypothesis nis correct.
Therefore. There is no way that the p value ncan be used to prove that the alternative hypothesis is true another way to show the pitfalls of blinding napplying p..
Value is to imagine a situation. Where a researcher flips. A coin 5. Times and ngets.
5 heads. In a row. If you performed a one tailed. Test you would nget a p value.
Of. 003 using the standard alpha of 005. This result nwould be deemed statically significant and we would reject the null hypothesis based solely on this data our conclusion would nbe that there is at least a 95 chance on subsequent flips of the coin that heads will nshow up significantly more often than tails. However we know this conclusion is incorrect nbecause.
The studies sample size was too small and there is plenty of external data to suggest nthat coins are fair given enough flips of the coin you will get heads about 50 of the ntime and tails about 50 of the time in actuality. The chance of the null hypothesis nbeing true is not 3 like we calculated but. Is actually 100 lastly we have statistical hypothesis testing nwhich is how we test the null hypothesis determine statistical significance for the usmle step. 1.
Medical board exam. All nyou need to know when to use the different tests you don u2019t need to know how to actually perform nthem. When you are comparing the mean or average nof. 2.
Groups you use the t test. When you are comparing the mean of 3 or more ngroups you use an anova test. When you are using categorical variables. Instead nof numerical variables.
You use a chi squared test. When using categorical values rather than nhaving a continuous numerical value that is measurable you have categories such as gender. Nor. The presence or absence of a disease that brings us to the end of the video.
I u2019d like to give a big thanks to brittany. Nhale dave carlson for going to my website stomponstep1com. And making donations which nhelped to fund this video. If you found this video useful.
Please comment nbelow as it really helps me out and if you would like to be taken directly nto. The next video in the series which will cover confidence intervals. You can click on nthis black box. Here.
If you are watching on a computer that video will be very much related to this none. So i definitely suggest checking it out thank you so much for watching and good luck nwith the rest of your studying. ” ..
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0:39 Null Hypothesis Definition
1:42 Alternative Hypothesis Definition
3:12 Type 1 Error (Type I Error)
4:16 Type 2 Error (Type II Error)
4:43 Power and beta
8:39 Alpha and statistical significance
14:15 Statistical hypothesis testing (t-test, ANOVA u0026 Chi Squared)
For the text of this video click here http://www.stomponstep1.com/p-value-null-hypothesis-type-1-error-statistical-significance/
For my video on Confidence Intervals click here http://www.stomponstep1.com/confidence-interval-interpretation-95-confidence-interval-90-99/
usmle step 1, What is a null hypothesis (and alternate hypothesis), Statistics 101: Null and Alternative Hypotheses – Part 1, Hypothesis testing and p-values…