Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the natural and social sciences to the humanities. Statistics is also used for making informed decisions in government and business.

There is a general perception that statistical knowledge is all-too-frequently intentionally misused, by finding ways to interpret the data that are favorable to the presenter. A famous saying attributed to Benjamin Disraeli is, "There are three kinds of lies: lies, damned lies, and statistics." And Harvard President Lawrence Lowell wrote in 1909 that statistics, "like veal pies, are good if you know the person that made them, and are sure of the ingredients."

If various studies appear to contradict one another, then the public may come to distrust such studies. For example, one study may suggest that a given diet or activity raises blood pressure, while another may suggest that it lowers blood pressure. The discrepancy can arise from subtle variations in experimental design, such as differences in the patient groups or research protocols, that are not easily understood by the non-expert. (Media reports sometimes omit this vital contextual information entirely.)

By choosing (or rejecting, or modifying) a certain sample, results can be manipulated; throwing out outliers is one means of doing so. Such manipulations need not be malicious or devious; they can arise from unintentional biases of the researcher. The graphs used to summarize data can also be misleading.

Deeper criticisms come from the fact that the hypothesis testing approach, widely used and in many cases required by law or regulation, forces one hypothesis (the null hypothesis) to be "favored", and can also seem to exaggerate the importance of minor differences in large studies. A difference that is highly statistically significant can still be of no practical significance. (See criticism of hypothesis testing and controversy over the null hypothesis.)

One response has been a greater emphasis on the p-value over simply reporting whether a hypothesis was rejected at the given level of significance. The p-value, however, does not indicate the size of the effect. Another increasingly common approach is to report confidence intervals. Although these are produced from the same calculations as hypothesis tests or p-values, they describe both the size of the effect and the uncertainty surrounding it.

[quote="AMANUSH-FORCE"][quote="rohiniranjan"][quote="AMANUSH-FORCE"][size=18][color=red]The Tenth House[/color][/size]
COURTESY---SJC siberia...................

Gday... [/quote]

SIBERIA? WOW!![/quote]
[b]
By choosing (or rejecting, or modifying) a certain sample, results can be manipulated; throwing out outliers is one means of doing so. Such manipulations need not be malicious or devious; they can arise from unintentional biases of the researcher. The graphs used to summarize data can also be misleading.

Deeper criticisms come from the fact that the hypothesis testing approach, widely used and in many cases required by law or regulation, forces one hypothesis (the null hypothesis) to be "favored", and can also seem to exaggerate the importance of minor differences in large studies. A difference that is highly statistically significant can still be of no practical significance. (See criticism of hypothesis testing and controversy over the null hypothesis.)

One response has been a greater emphasis on the p-value over simply reporting whether a hypothesis was rejected at the given level of significance. The p-value, however, does not indicate the size of the effect. Another increasingly common approach is to report confidence intervals. Although these are produced from the same calculations as hypothesis tests or p-values, they describe both the size of the effect and the uncertainty surrounding it.[/b][/quote]



Dear poster,

Are you copying and pasting from your statistics-101 notes or is there a relevance of the last post to what we are discussing and working with. Where did you get the p value or chi squared or similar in what has been discussed so far?

We are simply working with 'odds-ratios' so far, very ground-level stuff!

Actually, I felt that if in the total sample there are nearly 10% plus cases of "windfall" it cannot be termed as 'windfall'.
Secondly, if multiple occupational categories are listed implying an occultist being a writer and recieving a windfall too then the sample size exceeds 18000 K for occupational categories. I am not a scientist or a statistician but this is how I look at it.

There are certain interesting features to the data as well
If we use the jyotish hypothesis that if 8th Lord occupies Tenth then "subject to many varied factors" the 8th house matters are likely to get reflected in occupations/professions of the natives. ..then the data suggests that out of 18K sample we have 1419 cases of 8th Lord in Tenth ( roughly 8 percent which is only to be expected).
Out of these 1419 cases in around a 1000 cases the professions suggest Sex trade
Engineers
Science (hard core)
Religion (incl. cults)
Healers (all kinds)
Medical (incl chiropracters, DO)
Law (including intelligence types)
Military
Education
Politicians
Awards (windfalls)
Occultists
Writers
almost 70 %, very significant, suggesting that with this limited analysis there is a possible validation of the jyothish hypothesis.


Votive

[quote="Votive"]Actually, I felt that if in the total sample there are nearly 10% plus cases of "windfall" it cannot be termed as 'windfall'.
Secondly, if multiple occupational categories are listed implying an occultist being a writer and recieving a windfall too then the sample size exceeds 18000 K for occupational categories. I am not a scientist or a statistician but this is how I look at it.

There are certain interesting features to the data as well
If we use the jyotish hypothesis that if 8th Lord occupies Tenth then "subject to many varied factors" the 8th house matters are likely to get reflected in occupations/professions of the natives. ..then the data suggests that out of 18K sample we have 1419 cases of 8th Lord in Tenth ( roughly 8 percent which is only to be expected).
Out of these 1419 cases in around a 1000 cases the professions suggest Sex trade
Engineers
Science (hard core)
Religion (incl. cults)
Healers (all kinds)
Medical (incl chiropracters, DO)
Law (including intelligence types)
Military
Education
Politicians
Awards (windfalls)
Occultists
Writers
almost 70 %, very significant, suggesting that with this limited analysis there is a possible validation of the jyothish hypothesis.


Votive[/quote]

Dear Votive,

Can you elaborate a bit more for the kund-buddhis here like me

Not sure where you were planning to go with this analysis in your post!

We await eagerly your patient follow-up.

Should the 8th lord in 10th not create trouble for vocational success or one not able to deliver the promise that the soul made? <unless you, like me consider the hint given in sudarshana chart in BPHS as significant >