Conversation with Merlin [email protected] · Sun Dec 10 2023

Analyse this data table discuss trends in the bar graph ( i use with this data) aswell as error bars (representative of the standard deviation data): Table 2 Average Length of leaf and standard deviation Wavelengths Average Length of leaf (cm+/-) Standard deviation White (no film) 0.37 0.112857619 Blue 0.385 0.087509398 Green 0.285 0.098808693 Yellow 0.34 0.082078268 Red 0.32 0.100524938 No Light 0.045 0.088704121

Based on the data table you've provided, it indicates the average length of a leaf exposed to different wavelengths of light, along with corresponding standard deviations. However, as I do not have the actual bar graph you mentioned, I will discuss the trends based solely on the data provided.

Here are the key takeaways from the data:

1.Wavelength Impact on Average Leaf Length:**

  • The average leaf length is observed under blue light (0.385 cm), suggesting that this wavelength might be the most effective for leaf growth the ones tested.
  • The second-longest leaves are found under white light, which includes all visible wavelengths (0.37 cm).
  • Yellow light and red light exposure result in reduced average leaf sizes of 0.34 cm and 0.32 cm, respectively.
  • Green light has the least positive effect on the average leaf length (0.285 cm) compared to other colors with the exception of no light condition.

  1. Variation in Leaf Growth (Standardiation):

    • The standard deviation measures the variability or dispersion of leaf length data around the mean. A lower standard deviation indicates that the leaf lengths are more consistently close to the average, while a higher standard deviation suggests a wider variation in lengths.
    • The smallest standard deviation is observed under yellow light (0.082078268), suggesting that the leaf growth under yellow light is the most consistent.
    • Conversely, white light has the highest standard deviation (0.112857619), indicating the most variability in leaf length growth among the light conditions tested.

  2. Effect of Absence of Light:

    • Not surprisingly, the no-light condition has a dramatically negative effect on leaf growth, with an average leaf length of just 0.045 cm. is significantly lower than any of the light-exposed conditions. The standard deviation is comparatively moderate (0.088704121), which still indicates some variability despite the overall lower growth.

When using this data to create a bar graph, each color's wavelength of light could be represented by a separate with the height of the bar corresponding to the average leaf length. Error bars would extend from the top of each bar up down by the value of the standard deviation, illustrating the range within which most of the leaf lengths fall. Remember that the length of the error bars indicates the level of uncertainty or variability in the measurements: shorter error bars mean more confidence in the of the average length measurement, while longer error bars indicate less confidence.

It’s also crucial to note here that while the blue light shows the highest average leaf growth, the fact that the standard deviation under yellow light is the smallest might suggest that under yellow light, despite not providing the maximum average growth, the growth results are more predictable and consistent across samples. Conversely, although white light provides fairly average leaf growth, its largest standard deviation indicates less predictability in the outcome.

In the bar graph, the trends should generally show an increase in average leaf length from "No Light" conditions through green, red, yellow, and white light, peaking at blue. bar would have error bars—with No Light having relatively longer error in proportion to its bar height, depicting notable variation relative to its low average leaf length, and yellow having the shortest error bars, indicating the most consistent leaf length across that sample group.

-Identify trends -All have more growth except no light error bar -Error bars overlap (standard deviation) except for no light (comment how this is interesting)

From the data provided, let’s identify the trends and discuss the notable points regarding error bars and their overlaps.

  1. Trends in Leaf Growth:

    • All wavelength conditions (white, blue, green, yellow, red) promote leaf growth significantly more than the no light condition. This suggests that the presence of light, regardless of its color, is crucial for leaf growth.
    • The longest leaves on average are observed under blue light, indicating it may be the most beneficial wavelength for promoting leaf growth in this experiment.
    • Green light leads to the shortest leaves when compared to other colored lights, though it still supports more growth than no light at all.

  2. Analysis of Error Bars:

    • The error bars, which represent one standard deviation from the mean, indicate the variability in the data. Where error bars overlap between two conditions, it suggests that there is no statistically significant difference in leaf length between those conditions.
    • Based on the provided standard deviations, we would expect substantial overlap between the error bars of the colored light conditions (white, blue, green, yellow, red) because the standard deviations are relatively small, and the average leaf lengths are not drastically different from each other.
    • The no light condition stands out not only due to having the smallest average leaf length but also because its error bars would not overlap with those of the other conditions, since there is such a significant difference in growth. This lack of overlap highlights the statistical significance in the difference between no light conditions and any light conditions.

  3. Significance of Overlapping Error Bars:

    • The overlapping error bars between the light conditions (excluding no light) indicate that we cannot conclusively say one light condition leads to significantly greater leaf growth than the other with confidence unless the error bars do not overlap. This overlap means that while certain trends are observed (e.g., blue light has the highest average growth), the variability within each group does not allow us to confirm a definitive ranking order for leaf growth among these light conditions.
    • The exception of the no light condition is interesting because it shows that light, in general, has a statistically significant effect on leaf growth. The absence of overlap here indicates that light—regardless of color—makes a difference in growth when compared to complete darkness.

In summary, the data suggests a trend that light is necessary for leaf growth, with blue light potentially being most conducive among the tested colors. However, because of the overlapping error bars, the differences in leaf growth under different light colors (other than no light) are not pronounced enough to ensure a statistically significant ranking of their effectiveness based solely on this dataset. The distinct result for the no light condition underscores the importance of light for plant growth.

Using all of this knowledge provided below comment on the measurment of uncertanty and how this affects my data

Measurement of uncertainties The use of a ruler in cm created Uncertainty My uncertainty is high with 50% of the measurement being different How this affect my data :

Research Question: What is the effect of different wavelengths of light on the Leaf length using a ruler (cm+-) of cress plants (Lepidium sativum) after 5 days?

The curiosity to understand the effects of light on plant growth came from observing the plants near my windowsill. Plants kept near the window always seemed healthier and grow quicker, fuelling my interest in this subject. Undeniably, the observed vitality was due to the natural sunlight they received, containing an array of light wavelengths. Intrigued, I wanted to delve further into this observation, focusing on how different colours of light, essentially wavelengths, affect the growth of a common plant, the cress (Lepidium sativum). This research holds significance not only for my personal understanding but could also impact various realms, including agriculture, plant biology, and even space travel, where light conditions can be manipulated for optimal plant growth.

Background Information: Why do some wavelengths impact plant growth more than others? To answer this, we need to delve into the fundamental process of photosynthesis. In 'Lepidium sativum', as in all green plants, photosynthesis is primarily driven by pigments such as chlorophyll a, chlorophyll b, and carotenoids (B, Cornell.). These pigments absorb light most effectively at specific wavelengths, the absorption spectrum appearing to align with the action spectrum denoting the rate of photosynthesis at various light wavelengths. Interestingly, plants reflect green/yellow light, explaining the typical 'green' appearance of plants. It raises the question of how photosynthetic efficiency will change if plants are exposed to these less absorbed light colours.

Hypothesis: Based on my analysis, I hypothesise that the colour of light has a significant impact on the process of photosynthesis and overall growth in cress plants. I propose that exposing cress plants to blue and red lights will promote photosynthesis more effectively, leading to enhanced growth compared to those exposed to white light or no light. Conversely, I anticipate that plants exposed to white light and no light may exhibit slower growth due to the potential lack of stimulation of the necessary photosynthetic processes

Control Variables:

Seed Planting Medium: I must eliminate any potential nutrient-related growth interference; hence cress seeds will be planted in tissue paper, a non-nutrient medium. Watering: Overwatering or underwatering may impact plant growth. The paper towel will be kept moist with an initial wettering of the paper tissue to maintain an unchanged water provision of 50ml. Extrinsic Factors: Any unknown factors such as wind, ambient temperature, or light could potentially influence plant growth. Therefore, the plants will be enclosed within boxes to control these factors and ensure uniformity. Light Exposure Duration: The length of light exposure significantly affects photosynthetic rates; hence cress plants across all colour bands will receive the same light exposure duration for fair comparison and face the same light intensity of 1.9 watts from a lamp.

Title: Effect of Different Wavelengths of Light on the Leaf Length of Cress Plants (Lepidium sativum)

METHODS: The study aimed to understand the effect of different wavelengths of light on the leaf length of cress plants after five days. Building on pre-existing research on plant photosynthesis, the hypothesis that certain light colours (i.e., wavelengths) could have significant impacts on plant growth.

Apparatus The experimental setup (See Figure 1 - not provided) had different coloured light sources shining on cress plants inside light proof boxes. We employed light filters on specific lamps to manipulate the independent variable (IV) light wavelength. Please observe that the diagram labels all tools and methods involved in recording the dependent variable (DV) - leaf length, and controlling or monitoring variables.

IV Manipulation. Different colour filters (blue, red, green, yellow, No light, Normal white light) were applied to lamps that illuminated boxes containing cress plants.

CVs Control and Monitoring Control Variables (CVs) included the plant medium (tissue paper), watering conditions (initially moistened with 50 ml tap water), and light exposure duration across all samples (same intensity for all). The experimental setup controlled extrinsic factors such as wind and ambient temperature by enclosing the plants within the boxes. This ensured uniformity and minimised experimental variability. DV Assessment The dependent variable, leaf length, was quantitatively measured using a ruler (in cm) after five days. This data will allow us to observe if light colour has any quantifiable impact on leaf length in cress plants.

Statistical Methods Statistical analysis was performed using standard descriptive statistics to calculate mean leaf length under each light condition. Using Anova test for statistical analysis.

Safety, Ethics, Environment Safety precautions included monitoring box temperature to avoid overheating and ensuring seeds had access to oxygen for respiration. Ethically, the experiment did not involve any harm to living organisms. Environmentally, the study was controlled to limit any potential damage to surroundings, e.g., potential heat/fire hazards from lamps. Experiment The experimental procedures followed to investigate the effects of light wavelength on cress plant leaf length were designed carefully, utilising the aforementioned tools, and adhering to a strict protocol. Following the initial setup and the five-day exposure period, the top twenty largest leaves from each plant sample were measured, yielding the recorded data to be analysed subsequently.

Step-by-step Methodology:

  1. Gather all the necessary materials: Water measuring glass tube, 6 Cardboard boxes, 6 lamps (each emitting a minimum of 1.9 watts), different colour filters (blue, red, green, yellow), thermometer, 6 ziplock bags, paper towels, scissors, opaque tape, tap water (not distilled), a ruler, and a batch of 125 gardening cress seeds.
  2. Create an opening at the top of each cardboard box that is wide enough for the lamp to fit through.
  3. Attach the appropriate colour filter to each lamp, so that each lamp emits a different colour light (blue, red, green, yellow, white, and no filter).
  4. Fasten each lamp into the opening at the top of each cardboard box, ensuring the light shines into the box.
  5. Cut out a small hatch on the side of each box, large enough to place the ziplock bag containing the cress seeds and to later observe the growth.
  6. Use opaque tape (such as sellotape or duct tape) to seal any gaps in the box to prevent uncontrolled light from entering.
  7. Plug in each lamp and insert a thermometer into each box to check that the temperature does not exceed 25 degrees Celsius, wait 10-15 minutes to check this.
  8. Unfold and evenly space five paper towels on a flat surface. Using the water-measuring glass tube, measure 50ml of tap water and pour it evenly onto the paper towels.
  9. Sprinkle approximately 20 to 30 cress seeds onto the wet paper towels, ensuring the seeds are evenly distributed and not clumped together.
  10. Carefully place the wet paper towel with the seeds into each ziplock bag, making sure the seed side is facing upwards.
  11. Ensure the plastic ziplock bag is slightly open to allow oxygen to reach the seeds.
  12. Place the ziplock bag with the seeds inside the cardboard box, using the opening cut out earlier, with the seeds facing towards the light from the lamp. Close the hatch.
  13. Repeat steps 8 to 12 for each of the remaining cardboard boxes, altering the filter colour on the lamp per box.
  14. After five days, carefully remove the cress samples from each box.
  15. Measure the length of the top 20 biggest leaves of the cress plants from each box using a ruler and record the data.

Table 1 Sample of RAW Data (sample full data table will be listed in appendix) White 0.5 0.3 0.4 0.5 0.4 Blue 0.4 0.4 0.3 0.5 0.4 Green 0.2 0.4 0.3 0.4 0.3

Yellow 0.3 0.2 0.4 0.4 0.5 Red 0.4 0.5 0.3 0.4 0.3 No Light 0 0 0 0 0.1

Table 2 Average Length of leaf and standard deviation Wavelengths Average Length of leaf (cm+/-) Standard deviation White (no film) 0.37 0.112857619 Blue 0.385 0.087509398 Green 0.285 0.098808693 Yellow 0.34 0.082078268 Red 0.32 0.100524938 No Light 0.045 0.088704121

Data table The averages of the data of the full table are used to generate the graph. A sample size had to be used due to size limitations of said data table but the table can be found in the appendix.

Graph Using the Graph and Data table 2 the longest average leaf length is observed under blue light (0.385 cm), suggesting that this wavelength might be the most effective for leaf growth among the ones tested.The second longest leaves are found under white light, which includes all visible wavelengths (0.37 cm).Yellow light and red light exposure result in slightly reduced average leaf sizes of 0.34 cm and 0.32 cm, respectively.Green light has the least positive effect on the average leaf length (0.285 cm) compared to other colours with the exception of no light condition. All wavelength conditions (white, blue, green, yellow, red) promote leaf growth significantly more than the no light condition. This suggests that the presence of light, regardless of its colour, is crucial for leaf growth.

All wavelength conditions (white, blue, green, yellow, red) promote leaf growth significantly more than the no light condition. This suggests that the presence of light, regardless of its colour, is crucial for leaf growth. Green light leads to the shortest leaves when compared to other coloured lights, though it still supports more growth than no light at all. Moving on to the error bars, which represent one standard deviation from the mean, indicate the variability in the data. Where error bars overlap between two conditions, it suggests that there is no statistically significant difference in leaf length between those conditions. Based on the provided standard deviations (represented in data table 2), we would expect substantial overlap between the error bars of the coloured light conditions (white, blue, green, yellow, red) because the standard deviations are relatively small, and the average leaf lengths are not drastically different from each other.The no light condition stands out not only due to having the smallest average leaf length but also because its error bars would not overlap with those of the other conditions, since there is such a significant difference in growth. This lack of overlap highlights the statistical significance in the difference between no light conditions and any light conditions. The overlapping error bars between the light conditions (excluding no light) indicate that we cannot conclusively say one light condition leads to significantly greater leaf growth than the other with confidence unless the error bars do not overlap. This overlap means that while certain trends are observed (e.blue light has the highest average growth), the variability within each group does not allow us to confirm a definitive ranking order for leaf growth among these light conditions. However threw background research on absorption spectrum as well as somewhat varied levels of growth between the light based conditioned samples we cannot fully reject the null hypothesis that exposing cress plants to different colours of light (blue and red) will not result in any significant difference in the process of photosynthesis or overall growth when compared to cress plants exposed to white light or no light. To Reject the null hypothesis we must conduct an anova test.
Anova test Using an ANOVA test is essential for our analysis because it helps to statistically assess the likelihood that the observed differences in leaf growth under different light conditions are due to chance or are indeed significant. This is crucial, especially when error bars overlap, as it offers a robust method to compare multiple groups simultaneously and determine whether there is a statistically significant difference in growth that corresponds with the varying light conditions. Without ANOVA, we can't confidently assert that growth differences are not simply due to random variation.
Using stats kingdoms Anova Calculator we are able to deduce through a P value calculation. When the p-value is less than or equal to the chosen significance level (often denoted as α, typically 0.05), it suggests that there is enough evidence to reject the null hypothesis. In this case, it indicates that at least one of the group means is statistically different from the others. On the other hand, if the p-value is greater than the significance level, it implies that there is insufficient evidence to refute the null hypothesis, suggesting that there are no statistically significant differences among the group means. The Anova test carried out through statskingdom calculates a p value of -2.22e-16. This means the following:

  1. Hypothesis Check The null Hypothesis=H0 The hypothesis=H1 Since p-value<α, H0 is rejected. Some of the groups' averages are considered to be not equal. In other words, the difference between the averages of some groups is big enough to be statistically significant.
  2. P-value p-value equals -2.22045e-16, [p( x ≤ F ) = 1 ]. It means that the chance of type1 error (rejecting a correct H0) is small: -2.22e-16 (-2.2e-14%) The smaller the p-value the stronger the data supports H1 Using this information from the anova test calculator we have proven through our P value that we reject the null hypothesis, however through the use of the Tukey HSD test which tests for the statistical differences between each Sample group we can see that the no light sample slightly skews the data. (This can be seen in the full calculated P values and differences listed in the appendix) This is represented through the tukey's tests Significant differences in: x1-x6, x2-x3, x2-x6, x3-x6, x4-x6, x5-x6. (x1=White, x2=Blue, x3=Green, x4=Yellow, x5=Red, x6=No light) This means that the No light sample group skews the data. This means that we must test the data without this sample in order to check if the new data still rejects the null hypothesis.

Anova test run without no light sample group By rerunning the anova test we have now been able to test the hypothesis more accurately. Through the removal of the no light sample group the P value has been changed to 0.01249 which is still less than .5. Since p-value<α, H0 is rejected. Some of the groups' averages are considered to be not equal. In other words, the difference between the averages of some groups is big enough to be statistically significant. And the Tukey HSD test states The means of the following pair are significantly different: x2-x3. So blue and green the two data sets with the highest and lowest length in leaf size, respectively. And the null hypothesis is still rejected.

The measurement of uncertainty is a critical factor in experiments as itifies the potential deviations present in the obtained measurements. In your study of the effects of different wavelengths of light on leaf length, where a ruler was used as a measuring tool, acknowledging this uncertainty is important for interpreting the results accurately. Here's how the high uncertainty, with 50% of the measurements differing, affects your data:

. Increased Error Margins:

  • The use of a ruler introduces a degree of uncertainty in your measurements, particularly if there is difficulty in making precise readings. For example, if the ruler has increments in whole centimeters and the actual leaf sizes are not, this can introduce rounding errors.

  1. Reduced Measurement Precision:

    • With a 50% incidence of measurement differences, the precision of your data appears to be low. This means that individual measurements of leaf length may vary widely, leading to less confidence in determining the actual average length.

  2. Analysis Implications:

    • The reliability of statistical tests, such as the ANOVA you've used, depends on the precision of data. High uncertainty could potentially mask any significant differences between groups since the variability within the groups may be as large as the differences between the groups.

  3. Interpretation of Overlapping Error Bars:

    • Typically, error bars that overlap indicate no statistically significant difference between the means. However, with the high measurement uncertainty in your experiment, the meaning of overlapping error bars is not as clear. Components of these overlapping error bars could be due partly to the inherent variability of plant growth and partly to the measurement uncertainty.

  4. Impact on the Null Hypothesis:

    • The high uncertainty challenges the validity of rejecting the null hypothesis. Since your ANOVA results still suggest that differences between light conditions are statistically significant, even accounting for the high measurement uncertainty, it's likely that some genuine differences do exist the conditions of light exposure.

  5. Implications for Future Research: High levels of uncertainty highlight the need for more precise measuring tools or techniques in subsequent research. For reproducibility and accuracy, a digital caliper might the 50% variation in readings, leading to more reliable.

  6. Effect on Light Conditions Comparison:

    • The Tukey HSD test findings that the no-light significantly skews the data also underscore the need for precision in measurements. The statistically significant differences found between blue and green light provide some confirmation of your hypothesis, although the high uncertainty could still be obscuring the true extent of the effect of different wavelengths on leaf growth.

  7. Generalizing About Photosynthetic Efficiency:

    • While your results are compelling, the measurement uncertainty necessitates caution in drawing broad conclusions about photosynthetic efficiency under different light wavelengths. Even with a significant p-value after re-running the ANOVA the no-light group, the implications should be framed within the context of the measurement limitations.

In conclusion, the measure of uncertainty is vital to the interpretation of your experimental results. While statistical analyses indicate that there likely genuine differences in leaf growth under different light conditions, the high uncertainty calls for careful consideration potentially more precise measurement methods in future research to confirm these findings.

Make this into one paraggraph

Of course, here is the revised text in a single paragraph:

The measurement of uncertainty is critical in experiments, as it acknowledges the potential deviations present in obtained measurements. In your study on the effects of different wavelengths of light on leaf length, the use of a ruler as a measuring tool introduces a degree of uncertainty, particularly if there is difficulty in making precise readings. With a 50% incidence of measurement differences, the precision of your data appears to be low, leading to less confidence in determining the actual average length. The reliability of statistical tests, such as the ANOVA you've used, depends on the precision of data, and high uncertainty could potentially mask any significant differences between groups. Overlapping error bars can indicate that some genuine differences exist, despite the high measurement uncertainty. The high uncertainty challenges the validity of rejecting the null hypothesis, and it highlights the need for more precise measuring tools or techniques in subsequent research. The findings from the Tukey HSD test that the no-light significantly skews the data underscore the need for precision in measurements. While your results are compelling, the measurement uncertainty necessitates caution in drawing broad conclusions about photosynthetic efficiency under different light wavelengths. Even with a significant p-value after re-running the ANOVA for the no-light group, the implications should be framed within the context of the measurement limitations.