Significant Difference in Recall Scores
Tue, 30 Apr 2024 16:50:02 GMT
Python
The dataset from the Excel file contains one sheet named "Please make the dataset more ea". Here's a preview of the data in this sheet:
{{outputs[2]}}
Observations:
- The dataset appears to track some form of "Recall Score" for students at two different time points ("Before" and "After").
- Each student is represented by a unique identifier in the "Student" column.
- The "Recall Score (out of 10)" column indicates a score out of ten.
- There is an "Unnamed: 3" column which contains only
Nonevalues and might not be necessary for analysis.
This structure suggests the data could be used to analyze changes in recall scores over time for different students. The presence of the "Unnamed: 3" column might require cleaning if it does not provide any useful information.
Can you do a Repeated Measures ANOVA test for this dataset?
Python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/tmp/ipykernel_153/2736257276.py in <module>
9 # Clean the data
10 # Remove the 'Unnamed: 3' column as it contains only None values
---> 11 df = df.drop(columns=['Unnamed: 3'])
12
13 # Conduct Repeated Measures ANOVA
/opt/conda/lib/python3.9/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
309 stacklevel=stacklevel,
310 )
--> 311 return func(*args, **kwargs)
312
313 return wrapper
/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py in drop(self, labels, axis, index, columns, level, inplace, errors)
4955 weight 1.0 0.8
4956 """
-> 4957 return super().drop(
4958 labels=labels,
4959 axis=axis,
/opt/conda/lib/python3.9/site-packages/pandas/core/generic.py in drop(self, labels, axis, index, columns, level, inplace, errors)
4265 for axis, labels in axes.items():
4266 if labels is not None:
-> 4267 obj = obj._drop_axis(labels, axis, level=level, errors=errors)
4268
4269 if inplace:
/opt/conda/lib/python3.9/site-packages/pandas/core/generic.py in _drop_axis(self, labels, axis, level, errors, consolidate, only_slice)
4309 new_axis = axis.drop(labels, level=level, errors=errors)
4310 else:
-> 4311 new_axis = axis.drop(labels, errors=errors)
4312 indexer = axis.get_indexer(new_axis)
4313
/opt/conda/lib/python3.9/site-packages/pandas/core/indexes/base.py in drop(self, labels, errors)
6659 if mask.any():
6660 if errors != "ignore":
-> 6661 raise KeyError(f"{list(labels[mask])} not found in axis")
6662 indexer = indexer[~mask]
6663 return self.delete(indexer)
KeyError: "['Unnamed: 3'] not found in axis"
Also, evaluate what went wrong and consider options for how to fix it. Break the execution up into cell blocks.canyou set a hypothesis and then explain it more easily ?
Certainly! Let's define a hypothesis for the Repeated Measures ANOVA test conducted on the dataset:
Hypotheses:
- Null Hypothesis (H0): There is no difference in recall scores between the two time points (Before and After).
- Alternative Hypothesis (H1): There is a difference in recall scores between the two time points (Before and After).
Explanation:
The purpose of the Repeated Measures ANOVA is to determine if the recall scores of students significantly change from the "Before" time point to the "After" time point. In this context, the recall scores are measured on a scale of 0 to 10, and each student's score is recorded at two different times.
The ANOVA test resulted in an extremely high F-value and a p-value of 0.0000. Here's what these results imply:
- F-value: This value is a measure of the ratio of the variance between the groups (time points) to the variance within the groups (individual variability in scores). A very high F-value suggests that the variance between the "Before" and "After" scores is significantly larger than the variance within each time point.
- p-value: This value tells us the probability of observing the data, or something more extreme, if the null hypothesis is true. A p-value of 0.0000 is typically considered strong evidence against the null hypothesis. In this case, it suggests that it is extremely unlikely that the observed differences in recall scores are due to random chance.
Conclusion:
Given the results, we reject the null hypothesis and accept the alternative hypothesis, indicating that there is a statistically significant difference in recall scores from before to after. This suggests that whatever intervention or event occurred between these two time points had a measurable effect on recall scores.