Question

Apparent discrepancy resolve the apparent discrepancy between:

a) Theoretical and experimental data
b) Qualitative and quantitative analysis
c) Inconsistent observations
d) Hypothesis and conclusion

Answer 1

To resolve apparent discrepancies in science, further experiments and data collection are needed to test hypotheses. Qualitative and quantitative analysis can be used together to gain a comprehensive understanding. Inconsistent observations can be addressed by investigating potential factors that may have influenced them. Evaluating experimental data helps to determine the validity of a hypothesis and the corresponding conclusion.

Step-by-step explanation:

The apparent discrepancies between:

1. Theoretical and experimental data can be resolved by conducting further experiments and collecting more data. A hypothesis is tested through experiments, and if the experimental results contradict the theoretical predictions, the hypothesis needs to be discarded or revised. If the results agree with the predictions, it adds support to the hypothesis but does not prove it as absolute truth.
2. Qualitative and quantitative analysis can be resolved by using both types of analysis together. Qualitative analysis describes properties or occurrences without relying on numbers, while quantitative analysis involves measurements and numerical data. Combining both approaches can provide a more comprehensive understanding of the subject being studied.
3. Inconsistent observations can be resolved by examining the possible factors that may have influenced the observations and conducting further investigations. Inconsistencies could be due to errors in the experiment, external variables, or limitations of the measuring instruments. By identifying and addressing these factors, scientists can aim to obtain more consistent observations.
4. The discrepancy between a hypothesis and a conclusion can be resolved by analyzing the experimental data and evaluating the validity of the hypothesis. If the data support the hypothesis, it can be considered a valid explanation. However, if the data contradict the hypothesis, it is necessary to revise or reject the hypothesis and develop an alternative explanation.