did a biology experiment where I need to analyze the statistical significance of betweeen a data set. My experiment consisted of measuring the effects in photosynthesis and cellular respiration by exposing solutions with leaf discs to different light wavelength. For the statistical significance, I have to compare data I got with blue and green light to white light. The amount of discs for the time point I chose for blue was: 3, white: 23 and for green: 6 I'm supposed to use the chi square method to compare this values and I got 13 for my expected value between blue and white and 8.5 between green and white and then a chi square of 7.69 for blue/white and of 24.735 for green/white. Are those values right? I have to use this method for many other time points but if I have this one right probably I have the others right, same if I have it wrong, if they are wrong, please explain how to calculate it.
The equation I used was chi^2= (obs-exp)^2/exp I calculated my expected value by taking the average of the ones I have. Will my observed value be any of the two I have, or do I have the add them and that will be my observed value?
To determine if your calculated chi-square values are correct, we need to first understand the steps involved in calculating the chi-square statistic using the chi^2= (obs-exp)^2/exp equation.
1. Determine the observed values for each category or group. In your case, the observed values for blue, white, and green light are 3, 23, and 6, respectively.
2. Calculate the expected values for each category or group. In your case, the expected values can be obtained by taking the average of the observed values. So, the expected values for blue, white, and green light would be (3+23+6)/3 = 10, 10, and 10, respectively.
3. Substitute the observed and expected values into the chi-square equation: chi^2= (obs-exp)^2/exp. For the blue/white comparison, the observed and expected values are 3 and 10, respectively. Similarly, for the green/white comparison, the observed and expected values are 6 and 10, respectively. Plugging these values into the equation will give you the chi-square values for each comparison.
Now let's calculate the chi-square values for each comparison based on the given observed and expected values:
For blue/white comparison:
chi-square = [(3-10)^2]/10 = 49/10 = 4.9
For green/white comparison:
chi-square = [(6-10)^2]/10 = 16/10 = 1.6
Therefore, the correct chi-square values for blue/white and green/white comparisons are 4.9 and 1.6, respectively.
To assess statistical significance, you need to compare these chi-square values to the critical chi-square value at a specific level of significance (usually determined using a chi-square distribution table or statistical software). If the calculated chi-square value exceeds the critical value, there is a statistically significant difference between the observed and expected values.
Keep in mind that the chi-square method is just one way to analyze statistical significance, and it may have assumptions and limitations. It is always advisable to consult with your instructor or a statistics expert to ensure you are using the appropriate method for your specific experiment and data.