 3646400 divided by 51 and we get to the the 71 498 it's rounded as you can see so there we have that and this is just going to show us the division in excels taking the cell divided by this cell all right so then if I go to the right here we can look at a histogram here is a histogram of our data which is simply taking the buckets on the bottom and seeing how many of these items fall into the buckets from 67 9 to 69 364 you know you had between about 9 from 69 364 to 70 thousand 827 you up to like 15 and so on and so forth and you've got a couple that are still kind of outline a kind of outside of this particular histogram but the data set is not as that long either now let's add a significant outlier however so we've got the same data set but now at the bottom of it it jumps up we've got this one outlier which is significantly larger than the rest of the data set and it could be much larger than that even one million I mean it could be like you know 10 million or something you know they could really skew the numbers so what does that do when we look at our standard calculations so if we do if we do our standard calculations with the same data set but simply adding the outlier now we're at 87 or 89 354 as opposed to the 71 498 so it's a pretty significant change and if that outlier was a lot larger you would have even a more significant kind of change to the average so the outlier you know it will depend on how many numbers are in the data set and then how big that number is relative to the rest of the data set and then if I look at the median you can see that the median did not change so that's a huge indication notice that these two have a fairly significant difference so if I just looked at these two numbers versus these two numbers I'm more likely to say over here well maybe there's an outlier because the median and the mean are fairly are a little bit more significantly different from each other than over here so that's going to be an indication that also shows us that if there's something that has an outlier in it we have to we have to ask the question of what is our objective and which number would be best how are we going to deal with that if there's an outlier in it then we might say that it'd be better to take the middle number if I'm trying to for example see how much money I'm going to earn at a particular organization I probably can't pick the outliers probably going to skew that because I would I would assume if I'm an average person I would be somewhere in the middle but I can't take the average of all of them because that outlier kind of skews the whole average therefore you're likely to take the median that often happens with things like home prices for example if there's a million dollar home in the neighborhood or multiple million dollar home but most of the homes are around you know 200,000 or whatever then then that outliers going to really skew the numbers of what the home may actually cost the other thing that you could do is say well let me take all of the numbers and just trim off the outliers I'm going to remove the outliers and then take the mean or the average without those outliers in it so those are some strategies that you might take but obviously also realize that when there's an outlier that's another opportunity to kind of be a little bit deceptive with numbers depending on what they're trying to do you know if they're if they're trying if someone is trying to say I'm I live in a very wealthy area and get prestige or something or whatever for they could say well the average of my the average home price in my neighborhood happens is higher because they're taking into account outliers maybe right because you know some rich person happens to have a mansion that lives in the neighborhood or something like that and the when this happens of course in business all the time as well when people are trying to make an argument they're usually going to look for the statistics not to look for evidence to support a hypothesis in good faith unfortunately oftentimes they're looking for numbers to support their argument just like when they do with words that again it doesn't mean that it doesn't mean the numbers are wrong you can't blame the numbers you got you got to blame the person who's being deceptive with the number with the numbers right and then and then look at it from up from a fuller picture and see if you can pick up what is going on if we look at the maximum obviously the maximum has changed now because this is the highest number in the data set so this will give you a clear indication generally that there's an outlier if you take the max and the men there could be an outlier on the small side too so now you can say okay well yeah the now this outlier looks way higher than the average or the median and that's another indication that there's that there's this outlier issue that you're gonna have to deal with in some way in order to come up with a rational conclusion about whatever decision we're making and such as should I work for this place or how much am I likely to make in this place if I go in as an average person the men number did not change because there's no outlier on the men side of things the outliers on the max side of things now if one person like if there was if the CEO tried a bold strategy and said I'm not gonna earn any money unless the profits go up and they take $1 salary or something like that then it's possible you could have an outlier on the small side of $1 right which again would probably skew the numbers the other way not as far not as far as the million dollars but it would skew the numbers that way so then on quartile