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# Any statistics guru's here?

Can anyone help me carry out a t-test using Excel?

I tried using the 'formula' bar but I don't understand which data goes where..
Also, what does a high or a low result from a t-test mean?

Appreciate any help on this.

6 Comments

I've just copied and pasted this from a doc that I used to give out to my A level Biol students.

T Test to Compare Two Sets of Data
Another common form of data analysis is to compare two sets of measurements to see if they are the same or different. For example are plants treated with fertiliser taller than those without? If the means of the two sets are very different, then it is easy to decide, but often the means are quite close and it is difficult to judge whether the two sets are the same or are significantly different. To compare two sets of data use the t test, which tells you the probability (P) that there is no difference between the two sets. This is called the null hypothesis.

P varies from 0 (impossible) to 1 (certain). The higher the probability, the more likely it is that the two sets are the same, and that any differences are just due to random chance. The lower the probability, the more likely it is that that the two sets are significantly different, and that any differences are real. Where do you draw the line between these two conclusions? In biology the critical probability is usually taken as 0.05 (or 5%). This may seem very low, but it reflects the facts that biology experiments are expected to produce quite varied results. So if P > 5% then the two sets are the same (i.e. accept the null hypothesis), and if P
In Excel the t test is performed using the formula: =TTEST (range1, range2, tails, type) . For the examples you'll use in biology, tails is always 2 (for a "two-tailed" test), and type can be either 1 for a paired test (where the two sets of data are from the same individuals), or 2 for an unpaired test (where the sets are from different individuals). The cell with the t test P should be formatted as a percentage (Format menu > cell > number tab > percentage). This automatically multiplies the value by 100 and adds the % sign. This can make P values easier to read and understand. It’s also a good idea to plot the means as a bar chart with error bars to show the difference graphically.

If you're getting in to Statistics you should really be using a proper stats package like Minitab, R, STATA, Statistica etc. using Excel for stats is a bit like using string to hold up your trousers, it kinda works but it's just not right.

]Does this help?, it's hard giving this sort of help over t'internet.

What sort of data have you got and what do you want to show?

Original Poster

chesso;8076376

I've just copied and pasted this from a doc that I used to give out to my … I've just copied and pasted this from a doc that I used to give out to my A level Biol students.T Test to Compare Two Sets of DataAnother common form of data analysis is to compare two sets of measurements to see if they are the same or different. For example are plants treated with fertiliser taller than those without? If the means of the two sets are very different, then it is easy to decide, but often the means are quite close and it is difficult to judge whether the two sets are the same or are significantly different. To compare two sets of data use the t test, which tells you the probability (P) that there is no difference between the two sets. This is called the null hypothesis.P varies from 0 (impossible) to 1 (certain). The higher the probability, the more likely it is that the two sets are the same, and that any differences are just due to random chance. The lower the probability, the more likely it is that that the two sets are significantly different, and that any differences are real. Where do you draw the line between these two conclusions? In biology the critical probability is usually taken as 0.05 (or 5%). This may seem very low, but it reflects the facts that biology experiments are expected to produce quite varied results. So if P > 5% then the two sets are the same (i.e. accept the null hypothesis), and if P In Excel the t test is performed using the formula: =TTEST (range1, range2, tails, type) . For the examples you'll use in biology, tails is always 2 (for a "two-tailed" test), and type can be either 1 for a paired test (where the two sets of data are from the same individuals), or 2 for an unpaired test (where the sets are from different individuals). The cell with the t test P should be formatted as a percentage (Format menu > cell > number tab > percentage). This automatically multiplies the value by 100 and adds the % sign. This can make P values easier to read and understand. It’s also a good idea to plot the means as a bar chart with error bars to show the difference graphically.

Thanks repped for this info. Starting to make more sense now however I reckon I need an example to follow.. :thinking:

Original Poster

megalomaniac;8076457

If you're getting in to Statistics you should really be using a proper … If you're getting in to Statistics you should really be using a proper stats package like Minitab, R, STATA, Statistica etc. using Excel for stats is a bit like using string to hold up your trousers, it kinda works but it's just not right.

Not familiar with any of the others (have heard of minitab). Only really know how to use excel. Is it still possible?

greg_68;8076480

]Does this help?, it's hard giving this sort of help over t'internet.What … ]Does this help?, it's hard giving this sort of help over t'internet.What sort of data have you got and what do you want to show?

Thanks but Im not sure if it does.

Basically, I'm trying to compare to sets of data (variables) and need to show whether there is a connection or not.
Should I upload sum screen dumps to show the sort of data I have?

ele-font;8076578

Thanks repped for this info. Starting to make more sense now however I … Thanks repped for this info. Starting to make more sense now however I reckon I need an example to follow.. :thinking:

Slave driver!!

In the first example the yield of potatoes in 10 plots treated with one fertiliser was compared to that in 10 plots treated with another fertiliser. Fertiliser B delivers a larger mean yield, but the unpaired t-test P shows that there is a 8% probability that this difference is just due to chance. Since this is >5% we accept the null hypothesis that there is no significant difference between the two fertilisers.

In the second example the pulse rate of 8 individuals was measured before and after eating a large meal. The mean pulse rate is certainly higher after eating, and the paired t test P shows that there is only a tiny 0.005% probability that this difference is due to chance, so the pulse rate is significantly higher after a meal.
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