Hi guys,

I'm new to R and I'm trying to learn It by working on a small project.

My goal is to say whether a new value of blood pressure is "coherent" with the previous ones the patient has inserted or it is just a "random value".

The sample vector I'm working with is the following:

```
pMax <- c(119,120,121,122, 123,124,
119,120,121,122,123,124,
119,120,121,122,123,124,
119,120,121,122,123,124,
119,120,121,122,123,124,
120,120,120,120,120,120,120,120,120,120,120,120,120'
110, 110, 110,
140, 140, 140, 140)
```

Let's say that I assumed the "normal pressure values" are within the range 119-124 and the values 110 and 140 has been inserted with the only purpose of distract the script.

I tried many ways to achive so and I'm looking for any statistical/R advise.

**With standard deviation**

```
error <<- sd(pMax)*sqrt((length(pMax)-1)/length(pMax))
print(paste( "Interval pMax ", (mean(pMax, trim=0.5)-error ), " - ", ( mean(pMax, trim=0.5)+error) ))
```

which gives back

`"Interval pMax 113.877092194063 - 126.122907805937"`

*Don't like at all*

**Percentils**

```
sort(pMax)
print( quantile(pMax, c(0.05, 0.95)) )
```

which gives back

```
5% 95%
114.05 140.00
```

*Don't like* . I may reduce the percentils according to my needs but it cannot be applied to each patient I'm working with.

**T-test**

`print( t.test(pMax, mu=pNewValues, conf.level = 0.98) )`

gives back

```
One Sample t-test
data: pMax
t = -1.2576, df = 49, p-value = 0.2145
alternative hypothesis: true mean is not equal to 123
98 percent confidence interval:
119.7964 124.0036
sample estimates:
mean of x
121.9
```

and this results looks acceptable.

The range it gives back is at 98% confidence is the same I expect (eventually, I'll round both values to 119 and 25).

**pNewValue** is a random new value I insert into the script to manually check whether it is "coherent" with the previous one or not.

Now a couple of questions:

- I can't get why for values
**higher**than the average got from the t.test() - reaching the**upper limit**of the range - p-value tends time by time to 0 (0.0004386) whereas going towards the**lower limit**tends to 1. Shouldn't it that - for values closer to the average - the value is supposed not to be "random" and p-value <0.005? - t.test() as far as I read is applied to only
*chisquared distributions*and not to**normal distribution**. am I asked to check first the "skew" both sides before proceeding?

Thank you for any help or any discussion!

N.