Which Value to Use: % Discrepancy or Standard Deviation?

In summary, the conversation discusses an experiment on interference of light passing through two slits. The individual is uncertain about which result value to use, either % discrepancy or standard deviation. The data shows a discrepancy of around 7 and a difference between the theoretical and experimental values of less than one standard deviation. It is mentioned that a normal qq plot on the data and the log of the data can be used to determine the appropriate value to use. However, the individual expresses difficulty in utilizing these concepts.
  • #1
August Lee
1
0
I did an experiment about interference of light when it passes two slits. I got datas and made chart, but I'm confused which result value I need to use whether % discrepancy or standard deviation. My data's & discrepancy is around 7, and difference between theoretical value and experiment value is less than one standard deviation. How can I state about this experiment? I know their concept but I cannot utilize them :(

Thanks in advance!
 
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  • #2
That is largely a judgement call. I usually do a normal qq plot on the data and another on the log of the data (essentially the percent discrepancy). If the log transformed data is much more normal then I will use it, but if they are about the same then I will use the untransformed data.
 

FAQ: Which Value to Use: % Discrepancy or Standard Deviation?

1. How do you determine which statistical tests to use when analyzing data from an experiment?

The choice of statistical tests depends on the type of data collected and the research question being investigated. Generally, parametric tests are used for normally distributed continuous data, while non-parametric tests are used for non-normally distributed data or categorical data. It is important to consult with a statistician or use online resources to determine the appropriate tests for your specific data.

2. What is the difference between descriptive and inferential statistics when analyzing data from an experiment?

Descriptive statistics involve summarizing and describing the characteristics of the data, such as measures of central tendency and variability. Inferential statistics, on the other hand, involve making inferences and generalizations about a larger population based on the data collected from a smaller sample. Descriptive statistics are used to understand the data, while inferential statistics are used to draw conclusions and make predictions.

3. How do you handle missing data when analyzing data from an experiment?

Missing data can be handled in several ways, depending on the reason for the missing values and the type of analysis being conducted. If the missing data is random, it can be excluded from the analysis. However, if the missing data is non-random, imputation techniques can be used to estimate the missing values. It is important to carefully consider the best approach for handling missing data in order to avoid bias in the results.

4. What are some common mistakes to avoid when analyzing data from an experiment?

Some common mistakes to avoid when analyzing data from an experiment include: not clearly defining the research question or hypothesis, not using appropriate statistical tests, not considering the assumptions of the statistical tests, not properly handling outliers or missing data, and not interpreting the results in the context of the research question. It is important to carefully plan and execute the data analysis process in order to ensure accurate and meaningful results.

5. How can data visualization be useful when analyzing data from an experiment?

Data visualization, such as graphs and charts, can be useful in understanding and communicating the results of an experiment. It can help identify patterns, trends, and relationships in the data that may not be apparent from numerical summaries alone. Data visualization can also be used to effectively present the results to others, making it a valuable tool in the data analysis process.

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