Color Of Bees Lab Report
Independent Variable- colours of five artificial flowers. Colours used: yellow, red, blue, light violet, white.
Dependent Variable- Amount of bees attracted by the colours (± 1 bee).
Raw Data-
Colour Amount of bees every five minutes (± 0.1 s; ±1 bee) Trial 1 Trial 2 Trial 3 Trial 4 Trial 5
Red 0 0 0 0 0
Yellow 2 3 5 2 7
Blue 0 0 1 1 1
Light Violet 1 1 0 1 0
White 2 2 1 1 1
Table 1- Raw data.
Qualitative Data-
At the beginning of the experiment the weather was cloudy, while at the end of the experiment the sun came out. Since my experiment lasted for about 25-30 minutes, the weather may have affected my data. In fact, bees are more likely to leave the house when the weather outside is warm.
The bee hives were all placed in the same area, …show more content…
Σ〖(x-x̅)〗^2=4+1+1+4+9=19 degree of freedom=number of scores-1=4
Standard deviation=√((Σ〖(x-x̅)〗^2)/(degree of freedom))=2.17
As we can't have 2.17 bees, we need to approximate the values to the closer number. For this example the value changes from 2.17 to 2. The standard error is a way to measure the accuracy of different means. It is calculated dividing the standard deviation by the roots of the number of trials.
Example for the yellow colour:
Standard Error=(standard deviation)/√5=0.97
Also for the standard error, we need to approximate the values to the closer number. So here the standard error changes from 0.97 to 1. To the uncertainty of the mean shows how far the mean can be wrong. To calculate it you need to add and subtract twice the values of the standard error.
Colour Mean (±1) Standard Deviation Standard Error Uncertainties of means
Red 0 0 0 0 ± 0
Yellow 4 2 1 4 ± 2
Blue 1 1 0 1 ± 0
Light Violet 1 1 0 1 ± 0
White 1 1 0 1 ± 0
Table 2- Showing the mean of the amount of bees attracted to each colour, the standard deviation, the standard error and the uncertainties of the means. All these values have been approximated to the closer …show more content…
I placed the five different artificial flowers at the same distance from the beehive so that the bees wouldn't see the closer flower first. To attract more bees I used some honey as attractant- I placed 20 ml of honey on the top of each flower. When I planned the investigation, I hypothesized that bees are more attracted to the yellow colour as bees perceive colours in a different way from humans and yellow is perceived as a bright colour. I also predicted that bees wouldn't be attracted to red colour as it is perceived as black and bees are warned off by dark colours.
From my results, I can say that bees prefer yellow above all the other colours (mean 4 ± 2). Moreover, during the 25 minutes of my experiment, not even one bee lied down on the red flower. Overall, the colours blue, light violet and white attracted equally amounts of bees. This shows that my hypothesis was right: bees are more attracted by the colour yellow and warned off by red colour as they perceive it as
Dependent Variable- Amount of bees attracted by the colours (± 1 bee).
Raw Data-
Colour Amount of bees every five minutes (± 0.1 s; ±1 bee) Trial 1 Trial 2 Trial 3 Trial 4 Trial 5
Red 0 0 0 0 0
Yellow 2 3 5 2 7
Blue 0 0 1 1 1
Light Violet 1 1 0 1 0
White 2 2 1 1 1
Table 1- Raw data.
Qualitative Data-
At the beginning of the experiment the weather was cloudy, while at the end of the experiment the sun came out. Since my experiment lasted for about 25-30 minutes, the weather may have affected my data. In fact, bees are more likely to leave the house when the weather outside is warm.
The bee hives were all placed in the same area, …show more content…
Σ〖(x-x̅)〗^2=4+1+1+4+9=19 degree of freedom=number of scores-1=4
Standard deviation=√((Σ〖(x-x̅)〗^2)/(degree of freedom))=2.17
As we can't have 2.17 bees, we need to approximate the values to the closer number. For this example the value changes from 2.17 to 2. The standard error is a way to measure the accuracy of different means. It is calculated dividing the standard deviation by the roots of the number of trials.
Example for the yellow colour:
Standard Error=(standard deviation)/√5=0.97
Also for the standard error, we need to approximate the values to the closer number. So here the standard error changes from 0.97 to 1. To the uncertainty of the mean shows how far the mean can be wrong. To calculate it you need to add and subtract twice the values of the standard error.
Colour Mean (±1) Standard Deviation Standard Error Uncertainties of means
Red 0 0 0 0 ± 0
Yellow 4 2 1 4 ± 2
Blue 1 1 0 1 ± 0
Light Violet 1 1 0 1 ± 0
White 1 1 0 1 ± 0
Table 2- Showing the mean of the amount of bees attracted to each colour, the standard deviation, the standard error and the uncertainties of the means. All these values have been approximated to the closer …show more content…
I placed the five different artificial flowers at the same distance from the beehive so that the bees wouldn't see the closer flower first. To attract more bees I used some honey as attractant- I placed 20 ml of honey on the top of each flower. When I planned the investigation, I hypothesized that bees are more attracted to the yellow colour as bees perceive colours in a different way from humans and yellow is perceived as a bright colour. I also predicted that bees wouldn't be attracted to red colour as it is perceived as black and bees are warned off by dark colours.
From my results, I can say that bees prefer yellow above all the other colours (mean 4 ± 2). Moreover, during the 25 minutes of my experiment, not even one bee lied down on the red flower. Overall, the colours blue, light violet and white attracted equally amounts of bees. This shows that my hypothesis was right: bees are more attracted by the colour yellow and warned off by red colour as they perceive it as